In recent years, the introduction of neural networks (NNs) into the field of speech enhancement has brought significant improvements. However, many of the proposed methods are quite demanding in terms of computational complexity and memory footprint. For the application in dedicated communication devices, such as speakerphones, hands-free car systems, or smartphones, efficiency plays a major role along with performance. In this context, we present an efficient, high-performance hybrid joint acoustic echo control and noise suppression system, whereby our main contribution is the post-filter NN, performing both noise and residual echo suppression. The preservation of nearend speech is improved by a Bark-scale auditory filterbank for the NN postfilter. The proposed hybrid method is benchmarked with state-of-the-art methods and its effectiveness is demonstrated on the ICASSP 2023 AEC Challenge blind test set. We demonstrate that it offers high-quality nearend speech preservation during both double-talk and nearend speech conditions. At the same time, it is capable of efficient removal of echo leaks, achieving a comparable performance to already small state-of-the-art models such as the end-to-end DeepVQE-S, while requiring only around 10% of its computational complexity. This makes it easily realtime implementable on a speakerphone device.

Acoustic echo cancellation (AEC) and suppression (AES) are widely researched topics. However, only few papers about hybrid or deep acoustic echo control provide a solid comparative analysis of their methods as it was common with classical signal processing approaches. There can be distinct differences in the behaviour of an AEC/AES model which cannot be fully represented by a single metric or test condition, especially when comparing classical signal processing and machine-learned approaches. These characteristics include convergence behaviour, reliability under varying speech levels or far-end signal types, as well as robustness to adverse conditions such as harsh nonlinearities, room impulse response switches or continuous changes, or delayed echo. A first contribution of this article is to present an extended set oftest conditionsandmetricsthat yields a proper characterization of an AEC/AES model and provides researchers with a useful toolbox to benchmark their systems. Second, we evaluate multiple AEC/AES models, each representing a classical, machine-learned, or hybrid paradigm, in various test conditions. We provide an analysis and new insights into their strengths and weaknesses and identify limitations of common metrics in some cases. Our entire toolbox of evaluation metrics and testing conditions is available on GitHub1.

In this work, we present a universal codebook-based speech enhancement framework that relies on a single codebook to encode both speech and noise components. The atomic speech presence probability (ASPP) is defined as the probability that a given codebook atom encodes speech at a given point in time. We develop ASPP estimators based on binaural cues including the interaural phase and level difference (IPD and ILD), the interaural coherence magnitude (ICM), as well as a combined version leveraging the full interaural transfer function (ITF). We evaluate the performance of the resulting ASPP-based speech enhancement algorithms on binaural mixtures of reverberant speech and real-world noise. The proposed approach improves both objective speech quality and intelligibility over a wide range of input SNR, as measured with PESQ and binaural STOI metrics, outperforming two binaural speech enhancement benchmark methods. We show that the proposed ITF-based ASPP approach achieves a good balance of the trade-off between binaural noise reduction and binaural cue preservation.

The problem of estimating the a priori signal-to-noise ratio (SNR) for single-channel speech enhancement is addressed. Similar to the decision-directed approach we linearly combine the maximum likelihood estimate of the a priori SNR with an estimate obtained from the previous frame. Based on the harmonic model for voiced speech we propose to smooth the a priori SNR estimate along harmonic trajectories instead of fixed discrete Fourier transform frequency bins. We interpolate by using a pitch-adaptive zero-padding in order to obtain the spectral coefficients at harmonic frequencies. The resulting pitch-adaptive decision-directed (PADDi) method increases the noise attenuation compared to the classical decision-directed approach and outperforms benchmark methods in terms of speech enhancement performance for several noise types at different SNRs, Quantified by objective evaluation criteria.

Speech enhancement methods formulated in the short-time Fourier transform (STFT) domain vary in the statistical assumptions made on the STFT coefficients, in the optimization criteria applied or in the models of the signal components. Recently, approaches relying on a stochastic-deterministic speech model have been proposed. The deterministic part of the signal corresponds to harmonically related sinusoids, often used to represent voiced speech. The stochastic part models signal components that are not captured by the deterministic components. In this paper, we consider this scenario under a new perspective yielding three main contributions. First, a pitch-synchronous signal representation is considered and shown to be advantageous for the estimation of the harmonic model parameters. Second, we model the harmonic amplitudes in voiced speech as random variables with frequency bin dependent Gamma distributions. Finally, distinct estimators for the different models of voiced speech, unvoiced speech, and speech absence are derived. To select from the arising estimates, we take into account the mutual impact of detection and estimation by proposing a binary decision framework that is derived from a Bayesian risk function. The resulting pitch-synchronous stochastic-deterministic estimator outperforms several benchmark methods in terms of speech intelligibility and perceived quality predicted by instrumental measures for various noise types and different signal-to-noise ratios.

Phase-aware signal processing has received increasing interest in many speech applications. The success of phase-aware processing depends strongly on the robustness of the clean spectral phase estimates to be obtained from a noisy observation. In this paper, we propose a novel harmonic phase estimator relying on the phase invariance property exploiting relations between harmonics using the phase structure. We present speech quality results achieved in speech enhancement to justify the effectiveness of the proposed phase estimator compared to noisy phase and other phase estimation benchmarks.

We investigate single-channel speech enhancement using the double spectrum (DS) consisting of pitch-synchronous and modulation transforms. We first explore the fundamentals of the proposed DS domain and its advantageous properties for pitch estimation and speech presence probability estimation. We then propose speech enhancement methods based on adaptive weighting and Wiener filtering in the DS domain. We demonstrate the effectiveness of the proposed DS-based methods compared to the conventional benchmarks in the modulation or short-time Fourier transform domains. Our results show a good tradeoff between improved perceived quality and slight degradation in speech intelligibility is achieved by the proposed method across different signal-to-noise ratios and noise types.

Conventional single-channel speech enhancement is mainly focused on modifying the noisy short-time Fourier transform amplitude spectrum while for signal reconstruction the noisy phase is used. Recent advances demonstrate the positive improvements in speech enhancement when the noisy phase is replaced with an estimated clean phase for signal reconstruction. In this paper, we study the impact of the linear phase and unwrapped phase components provided by harmonic phase decomposition on the speech quality at signal reconstruction. We present objective and subjective results comparing the proposed harmonic phase modification with other phase estimation methods. Our results show that enhancement of decomposed phase parts suffices for improved reconstruction in speech enhancement.

To approximate the speech quality of a given speech enhancement system, most of the existing instrumental metrics rely on the calculation of a distortion metric defined between the clean reference signal and the enhanced signal in the spectral amplitude domain. Several recent studies have demonstrated the effectiveness of employing a phase modification stage in single-channel speech enhancement showing positive impact brought by modifying both amplitude and phase in contrast to the conventional methods where the noisy spectral amplitude is only modified and noisy phase is used for signal reconstruction. In this work we present two contributions; First we study the reliability of the existing instrumental metrics for performance evaluation of phase-aware methods, and second we propose novel phase-aware instrumental metrics and evaluate their reliability in terms of predicting the perceived quality achieved by the phase-aware methods. Our objective and subjective evaluations demonstrate that PESQ and the proposed phase deviation metric perform as reliable speech quality estimators following the subjective results.

In single-channel speech enhancement the spectral amplitude of the noisy signal is often modified while the noisy spectral phase is directly employed for signal reconstruction. Recently, additional improvement in speech enhancement performance has been reported when the noisy phase is modified. In this work, we propose a Bayesian estimator for phase of harmonics given the noisy speech. The proposed estimator relies on the fundamental frequency and the signal-to-noise ratio at harmonics. Throughout our experiments, we evaluate the performance of the proposed phase enhancement in comparison with the noisy phase, a benchmark and the clean phase as the upper-bound. The proposed method leads to joint improvement in quality and intelligibility at different SNRs and noise types.

To reduce time and costs in the development process of noise reduction algorithms, an objective intelligibility measure is crucial. Such a measure has to show high correlation with speech intelligibility determined by real listening experiments. In the past several measures were found that perform reliable in a particular scenario when only the spectral amplitude of a noisy signal is modified. Recent studies demonstrate the positive impact of a phase modification in a single-channel speech enhancement showing improved speech intelligibility while conventional methods relying on amplitude-only modification are known for reduced intelligibility. Further, another recent study shows that a distortion metric defined on the spectral phase outperforms state-of-the-art quality metrics when used in phaseaware speech enhancement. This raises two questions we account for in this work; First, to study the reliability of the existing intelligibility measures in predicting the performance of the phase-aware methods, and second to investigate candidates for new phase-aware instrumental metrics and evaluate their reliability in terms of intelligibility prediction. Our objective and subjective evaluations demonstrate that CSII-based and STOI as well as the proposed phase-aware metrics perform as reliable speech intelligibility estimators following the subjective results.

Conventional single-channel source separation (SCSS) algorithms are mostly focused on estimating the spectral amplitude of the underlying sources extracted from a mixture. The importance of phase information in source separation and its positive impact on improving the achievable performance is not adequately studied yet. In this work, we propose a phase estimation method to enhance the spectral phase of the underlying signals in SCSS framework. The proposed method relies on multi-pitch estimation and phase decomposition followed by applying temporal smoothing filters on the unwrapped mixture phase. We consider the combination of the proposed phase estimator with ideal binary mask and non-negative matrix factorization, as two well-known SCSS methods for separating the spectral amplitudes. Our results show that certain improvements in quality and intelligibility is achievable via replacing the mixture phase with the estimated one when reconstructing the sources. Index Terms: Phase estimation, single-channel source separation, perceived quality, speech intelligibility.

Conventional speech enhancement methods typically utilize the noisy phase spectrum for signal reconstruction. This letter presents a novel method to estimate the clean speech phase spectrum, given the noisy speech observation in single-channel speech enhancement. The proposed method relies on the phase decomposition of the instantaneous noisy phase spectrum followed by temporal smoothing in order to reduce the large variance of noisy phase, and consequently reconstructs an enhanced instantaneous phase spectrum for signal reconstruction. The effectiveness of the proposed method is evaluated in two ways: phase enhancement-only and by quantifying the additional improvement on top of the conventional amplitude enhancement scheme where noisy phase is often used in signal reconstruction. The instrumental metrics predict a consistent improvement in perceived speech quality and speech intelligibility when the noisy phase is enhanced using the proposed phase estimation method.

In this paper, we present an overview on the previous and recent methods proposed to estimate a clean spectral phase from a noisy observation in the context of single-channel speech enhancement. The importance of phase estimation in speech enhancement is inspired by the recent reports on its usefulness in finding a phase-sensitive amplitude estimation. We present a comparative study of the recent phase estimation methods and elaborate their limits. We propose a new phase enhancement method relying on phase decomposition and time-frequency smoothing filters. We demonstrate that the proposed time-frequency phase smoothing method successfully reduces the variance of the noisy phase at harmonics. Our results on different speech and noise databases and different signal-to-noise ratios show that in contrast to the existing benchmark methods only the proposed method balances a tradeoff between a joint improvement in perceived quality of 0.2 in PESQ score and speech intelligibility of 2% by phase-only enhancement.

In conventional single-channel speech enhancement, typically the noisy spectral amplitude is modified while the noisy phase is used to reconstruct the enhanced signal. Several recent attempts have shown the effectiveness of utilizing an improved spectral phase for phase-aware speech enhancement and consequently its positive impact on the perceived speech quality. In this paper, we present a harmonic phase estimation method relying on fundamental frequency and signal-to-noise ratio (SNR) information estimated from noisy speech. The proposed method relies on SNR-based time-frequency smoothing of the unwrapped phase obtained from the decomposition of the noisy phase. To incorporate the uncertainty in the estimated phase due to unreliable voicing decision and SNR estimate, we propose a binary hypothesis test assuming speech-present and speech-absent classes representing high and low SNRs. The effectiveness of the proposed phase estimation method is evaluated for both phase-only enhancement of noisy speech and in combination with an amplitude-only enhancement scheme. We show that by enhancing the noisy phase both perceived speech quality as well as speech intelligibility are improved as predicted by the instrumental metrics and justified by subjective listening tests.

Sum-product networks (SPNs) are a recently proposed type of probabilistic graphical models allowing complex variable interactions while still granting efficient inference. In this paper we demonstrate the suitability of SPNs for modeling log-spectra of speech signals using the application of artificial bandwidth extension, i.e. artificially replacing the high-frequency content which is lost in telephone signals. We use SPNs as observation models in hidden Markov models (HMMs), which model the temporal evolution of log short-time spectra. Missing frequency bins are replaced by the SPNs using most-probable-explanation inference, where the state-dependent reconstructions are weighted with the HMM state posterior. According to subjective listening and objective evaluation, our system consistently and significantly improves the state of the art.

In this paper, we present a novel system for enhancing a target speech corrupted in a non-stationary real-life noise scenario. The proposed system consists of one spatial beamformer based on GCC-PHAT-estimated time-delay of arrival followed by three postfilters applied in a sequential way, namely: Wiener filter, minimum mean square error estimator (MMSE) of the log-amplitude, and a model-driven postfilter (MDP) that relies on particular speech signal statistics captured by target speaker Gaussian mixture model. The beamformer accounts for the directional interferences while the MMSE speech enhancement suppresses the stationary background noise, and MDP contributes to suppress the non-stationary sources from the binaural mixture. In our evaluation, multiple objective quality metrics are used to report the speech enhancement and separation performance, averaged on the CHiME development set. The proposed system performs better than standard state-of-the-art techniques and shows comparable performance with other systems submitted to the CHiME challenge. More precisely, it is successful in suppressing the non-stationary interfering sources at different SNR levels supported by the relatively high scores for signal-to-interference-ratio.

In this paper, we study the impact of exploiting the spectral phase information to further improve the speech quality of the single-channel speech enhancement algorithms. In particular, we focus on the two required steps in a typical single-channel speech enhancement system, namely: parameter estimation solved by a minimum mean square error (MMSE) estimator of the speech spectral amplitude, followed by signal reconstruction stage, where the observed noisy phase is often used. For the parameter estimation stage, in contrast to conventional Wiener filter, a new MMSE estimator is derived which takes into account the clean phase information as a prior information. In our experiments, we show that by including the phase information in the two steps, it is possible to improve the perceived signal quality of the enhanced signal significantly with respect to the methods that do not employ the phase information.

Partial phase reconstruction based on a confidence domain has recently been shown to provide improved signal reconstruction performance in a single-channel source separation scenario. In this paper, we replace the previous binarized fixed-threshold confidence domain with a new signal-dependent one estimated by employing a sinusoidal model to be applied on the estimated magnitude spectrum of the underlying sources in the mixture. We also extend the sinusoidal-based confidence domain into Multiple Input Spectrogram Inversion (MISI) framework, and we propose to re-distribute the remixing error at each iteration on the sinusoidal-signal components. Our experiments on both oracle and estimated spectra show that the proposed method achieves improved separation results at a lower number of iterations, making it as a favorable choice for faster phase estimation.

I4U is a joint entry of nine research Institutes and Universities across 4 continents to NIST SRE 2012. It started with a brief discussion during the Odyssey 2012 workshop in Singapore. An online discussion group was soon set up, providing a discussion platform for different issues surrounding NIST SRE’12. Noisy test segments, uneven multi-session training, variable enrollment duration, and the issue of open-set identification were actively discussed leading to various solutions integrated to the I4U submission. The joint submission and several of its 17 sub-systems were among top-performing systems. We summarize the lessons learnt from this large-scale effort.

Previous studies on performance evaluation of single-channel speech separation (SCSS) algorithms mostly focused on automatic speech recognition (ASR) accuracy as their performance measure. Assessing the separated signals by different metrics other than this has the benefit that the results are expected to carry on to other applications beyond ASR. In this paper, in addition to conventional speech quality metrics (PESQ and SNRloss), we also evaluate the separation systems output using different source separation metrics: blind source separation evaluation (BSS EVAL) and perceptual evaluation methods for audio source separation (PEASS) measures. In our experiments, we apply these measures on the separated signals obtained by two well-known systems in the SCSS challenge to assess the objective and subjective quality of their output signals. Comparing subjective and objective measurements shows that PESQ and PEASS quality metrics predict well the subjective quality of separated signals obtained by the separation systems. From the results it is observed that the short-time objective intelligibility (STOI) measure predict the speech intelligibility results.

Single-channel speech separation algorithms frequently ignore the issue of accurate phase estimation while reconstructing the enhanced signal. Instead, they directly employ the mixed-signal phase for signal reconstruction which leads to undesired traces of the interfering source in the target signal. In this paper, assuming a given knowledge of signal spectrum amplitude, we present a solution to estimate the phase information for signal reconstruction of the sources from a single-channel mixture observation. We first investigate the effectiveness of the proposed phase estimation method employing known magnitude spectra of sources as an ideal case. We further relax the ideal signal spectra assumption by perturbing the clean signal spectra via Gaussian noise. The results show that for both scenarios, ideal and noisy magnitude signal spectra, the proposed phase estimation approach offers improved signal reconstruction accuracy, segmental SNR and PESQ compared to benchmark methods, and those neglecting the phase information.

Model-based approaches to achieve Single Channel Source Separation (SCSS) have been reasonably successful at separating two sources. However, most of the currently used model-based approaches require pre-trained speaker specific models in order to perform the separation. Often, insufficient or no prior training data may be available to develop such speaker specific models, necessitating the use of a speaker independent approach to SCSS. This paper proposes a speaker independent approach to SCSS using sinusoidal features. The algorithm develops speaker models for novel speakers from the speech mixtures under test, using prior training data available from other speakers. An iterative scheme improves the models with respect to the novel speakers present in the test mixtures. Experimental results indicate improved separation performance as measured by the Perceptual Evaluation of Speech Quality (PESQ) scores of the separated sources.

In this paper, we present a novel system for joint speaker identification and speech separation. For speaker identification a single-channel speaker identification algorithm is proposed which provides an estimate of signal-to-signal ratio (SSR) as a by-product. For speech separation, we propose a sinusoidal model-based algorithm. The speech separation algorithm consists of a double-talk/single-talk detector followed by a minimum mean square error estimator of sinusoidal parameters for finding optimal codevectors from pre-trained speaker codebooks. In evaluating the proposed system, we start from a situation where we have prior information of codebook indices, speaker identities and SSR-level, and then, by relaxing these assumptions one by one, we demonstrate the efficiency of the proposed fully blind system. In contrast to previous studies that mostly focus on automatic speech recognition (ASR) accuracy, here, we report the objective and subjective results as well. The results show that the proposed system performs as well as the best of the state-of-the-art in terms of perceived quality while its performance in terms of speaker identification and automatic speech recognition results are generally lower. It outperforms the state-of-the-art in terms of intelligibility showing that the ASR results are not conclusive. The proposed method achieves on average, 52.3% ASR accuracy, 41.2 points in MUSHRA and 85.9% in speech intelligibility.

Speech enhancement and separation algorithms frequently employ two-stage processing schemes, where the signal is first mapped to an intermediate low-dimensional parametric description. Then, these parameters are mapped to vectors in codebooks trained on individual noise-free sources using a vector quantizer. To obtain accurate parameters, one must employ an estimator that takes the signal characteristics into account. An open question is, however, how to derive metrics for use in the vector quantization process. In this paper, we present and derive a new metric aimed at exactly this, and we exemplify and demonstrate its use in sinusoidal modeling. The metric takes into account that parameters may have different uncertainties and dependencies associated with them and thus leads to more accurate estimates, as is demonstrated in experiments. Moreover, we incorporate the metric in a recently proposed speech separation algorithm and compare its performance to state-of-the-art methods.

Most of the single-channel speech separation (SCSS) systems use the short-time Fourier transform as their parametric features. Recent studies have shown that employing sinusoidal features for the SCSS application results in a high perceived speech quality. In this paper, we make a systematic study on automatic speech recognition results for a SCSS system that uses sinusoidal features composed of amplitude and frequency. We compare the speech recognition results with those already reported by other participants in the single-channel speech separation and recognition challenge. Our results show that a newly proposed system achieves an overall recognition accuracy of 52.3%, ranges at the median over all other participants in the challenge. Index Terms: sinusoidal modeling, single-channel speech separation and recognition challenge.

In this paper, we suggest applying tree structure on the sinusoidal parameters. The suggested sinusoidal coder is targeted to find the coded sinusoidal parameters obtained by minimizing a likelihood function in a least square (LS) sense. From a rate-distortion standpoint, we address the problem of how to allocate available bits among different frequency bands to code sinusoids at each frame. For further analyzing the quantization behavior of the proposed method, we assess the quantization performance with respect to other methods: the short-time Fourier transform (STFT) based coder commonly used for speech enhancement or separation, and the line spectral frequency (LSF) coder used in speech coding. Through extensive simulations, we show that the proposed quantizer leads to less spectral distortion as well as higher perceived quality for the re-synthesized signals based on the coded parameters in a model-based approach with respect to previous STFT-based methods. The proposed method lowers the complexity, and, due to its tree-structure, leads to a rapid search capability. It provides flexibility for use in many speaker-independent applications by finding the most likely frequency vectors selected from a list of frequency candidates. Therefore, the proposed quantizer can be considered an attractive candidate for model-based speech applications in both speaker-dependent and speaker-independent scenarios.

We present new results on single-channel speech separation and suggest a new separation approach to improve the speech quality of separated signals from an observed mixture. The key idea is to derive a mixture estimator based on sinusoidal parameters. The proposed estimator is aimed at finding sinusoidal parameters in the form of codevectors from vector quantization (VQ) codebooks pre-trained for speakers that, when combined, best fit the observed mixed signal. The selected codevectors are then used to reconstruct the recovered signals for the speakers in the mixture. Compared to the log-max mixture estimator used in binary masks and the Wiener filtering approach, it is observed that the proposed method achieves an acceptable perceptual speech quality with less cross-talk at different signal-to-signal ratios. Moreover, the method is independent of pitch estimates and reduces the computational complexity of the separation by replacing the short-time Fourier transform (STFT) feature vectors of high dimensionality with sinusoidal feature vectors. We report separation results for the proposed method and compare them with respect to other benchmark methods. The improvements made by applying the proposed method over other methods are confirmed by employing perceptual evaluation of speech quality (PESQ) as an objective measure and a MUSHRA listening test as a subjective evaluation for both speaker-dependent and gender-dependent scenarios.

We present a novel single-channel separation approach to improve the separation performance while recovering the signals from a mixture. The key idea in this research is to employ a mixture estimator based on unconstrained modified sinusoidal parameters. Compared to the mixmax (binary mask) and Wiener filter (softmask) approaches, the proposed approach works independently of pitch estimates. Furthermore, it is observed that it can achieve acceptable perceptual speech quality with less cross-talk at different signal-to-signal ratios while bringing down the complexity by replacing STFT with sinusoidal parameters. Improvementsmade by the proposed approach are demonstrated by employing PESQ as our objective measure and MUSHRA listening test as our subjective evaluation.

In this paper we present a new approach for binary and soft masks used in single-channel speech separation. We present a novel approach called the sinusoidal mask (binary mask and Wiener filter) in a sinusoidal space. Theoretical analysis is presented for the proposed method, and we show that the proposed method is able to minimize the target speech distortion while suppressing the crosstalk to a predetermined threshold. It is observed that compared to the STFT-based masks, the proposed sinusoidal masks improve the separation performance in terms of objective measures (SSNR and PESQ) and are mostly preferred by listeners.

In this paper, we propose a closed loop system to improve the performance of single-channel speech separation in a speaker independent scenario. The system is composed of two interconnected blocks: a separation block and a speaker identification block. The improvement is accomplished by incorporating the speaker identities found by the speaker identification block as additional information for the separation block, which converts the speaker-independent separation problem to a speaker-dependent one where the speaker codebooks are known. Simulation results show that the closed loop system enhances the quality of the separated output signals. To assess the improvements, the results are reported in terms of PESQ for both target and masked signals.

In this paper, we consider speaker identification for the co-channel scenario in which speech mixture from speakers is recorded by one microphone only. The goal is to identify both of the speakers from their mixed signal. High recognition accuracies have already been reported when an accurately estimated signal-to-signal ratio (SSR) is available. In this paper, we approach the problem without estimating SSR. We show that a simple method based on fusion of adapted Gaussian mixture models and Kullback-Leibler divergence calculated between models, achieves an accuracy of 97% and 93% when the two target speakers enlisted as three and two most probable speakers, respectively.

This paper describes a novel approach to improve monoaural speaker identification where two speakers are present in a single-microphone recording. The goal is to identify both of the underlying speakers in the given mixture. The proposed approach is composed of a double-talk detector (DTD) as a preprocessor and speaker identification back-end. We demonstrate that including the double-talk detector improves the speaker identification accuracy. Experiments on GRID corpus show that including the DTD improves average recognition accuracy from 96.53% to 97.43%.

Single-channel separation (SCS) is a challenging scenario where the objective is to segregate speaker signals from their mixture with high accuracy. In this research a novel framework called subband perceptually weighted transformation (SPWT) is developed to offer a perceptually relevant feature to replace the commonly used magnitude of the short-time Fourier transform (STFT). The main objectives of the proposed SPWT are to lower the spectral distortion (SD) and to improve the ideal separation quality. The performance of the SPWT is compared to those obtained using mixmax and Wiener filter methods. A comprehensive statistical analysis is conducted to compare the SPWT quantization performance as well as the ideal separation quality with other features of log-spectrum and magnitude spectrum. Our evaluations show that the SPWT provides lower SD values and a more compact distribution of SD, leading to more acceptable subjective separation quality as evaluated using the mean opinion score.

It is already demonstrated that selected features have a much larger effect to the overall performance in speech applications accuracy than the selected generative models have. In this paper, we propose subband perceptually weighted transformation (SPWT) applied on magnitude spectrum to improve the performance of single-channel separation scenario (SCSS). In particular, we compare three feature types namely, log-spectrum, magnitude spectrum and the proposed SPWT. A comprehensive statistical analysis is performed to evaluate the performance of a VQ-based SCSS framework in terms of the lower error bound. At the core of this approach are two trained codebooks of the quantized feature vectors of speakers, whereby the main evaluation for separation is performed. The simulation results show that the proposed transformation offers an attractive candidate to improve the separation performance of model-based SCSS. It is also observed that the proposed feature can result in a lower-error bound in terms of the spectral distortion (SD) as well as higher SSNR in comparison with other features.

In many signal processing applications including Audio and speech processing as well as other research areas of diagnosis of failures in rotating machinery, finding a compact representation of observations signals is often highly desirable. In this respect, chirplets have recently been introduced as an efficient tool for signal representation. However, determining the required number of chirp atoms for the perfect estimation of an observed signal is of great importance. In this research work we study the chirplets signal decomposition to find a compact representation for audio signals of music and speech. The number of chirplets to be used is determined by using the state-of-the-art information criteria. Simulation and experimental results show that by using 4 chirplets are enough for accurate signal representation of music while about 7 to 9 chirplets suffices for speech signal reconstruction. As another application the evaluations are also made on vibration signals. Finally, extracted chirplet atoms are employed to reconstruct the observed signals of music and speech. Subjective tests show acceptable results for signal reconstruction.

This work investigates the problem of sinusoidal parameter estimation including amplitude, frequency and phase in all kinds of audio signals including speech, music and mixtures using a nonlinear least-squares (NLS) estimator where the number of sinusoids is in general unknown. A solution to the sinusoid order selection problem is presented by employing the NLS method along with some information criteria (IC) such as minimum description length (MDL) or AIC. The simulation results demonstrate that the presented approach is successful in determining the number of sinusoids used in sinusoidal representation by using the information provided by well-known ICs’. It is demonstrated that the original audio signal can successfully be reconstructed in terms of both objective (SSNR) and subjective results (MOS).

In this paper, a new joint structure for noise reduction in a reverberant environment will be proposed. The proposed structure consists of an acoustic echo canceller (AEC) followed by a noise reduction stage like generalized side-lobe canceller (GSC). This configuration is called AEC-GSC. It improves noise cancellation of the GSC beamformer in the presence of acoustic echoes in highly reverberant environments where GSC-alone fails to work properly. The AEC section is accomplished by our recently proposed segment variable step-size proportionate normalized least mean squares (SVS-PNIMS) algorithm. The proposed AEC-GSC algorithm is evaluated through both computer simulations and experimental results. The results demonstrate that the proposed AEC-GSC algorithm performs significantly better than GSC-alone in terms of speech distortion parameters and resulting ERLE.