This letter investigates blind identification and compensation of Hammerstein systems, whose inputs are sparse in the frequency domain. In our analysis, the Hammerstein system comprises an antisymmetric invertible polynomial nonlinear curve followed by a minimum-phase linear system. We show analytically in a simple scenario and empirically in more complex situations that the Hammerstein system is identifiable with high probability, provided that the nonlinear component of the system reduces the sparsity of the input by a sufficiently large amount. Moreover, we propose a practical compensation procedure in the form of the Wiener system that provides a maximally sparse output when cascaded with the given Hammerstein system. Monte Carlo experiments show that the procedure is effective for Hammerstein systems with sufficiently strong nonlinearities submitted to inputs within a wide range of sparsity levels.

This letter presents a novel approach to blind recovery of audio signals that have been distorted by a memoryless, invertible, and smooth nonlinear function. We introduce a cost function consisting of a weighted sum of squared discrete cosine transform coefficients of the recovered signal, whose weights are obtained from the distorted signal itself and, thus, can adapt to different signal characteristics. In order to prevent undesired trivial solutions, we impose an either quadratic or linear equality constraint, the latter case with closed-form solution. Despite its simplicity, our method outperforms a recent sparsity-based solution for memoryless nonlinearity compensation, in audio and speech databases.

The wide availability of mobile devices with embedded microphones opens up opportunities for new applications based on acoustic sensor localization (ASL). Among them, this paper highlights mobile device self-localization relying exclusively on acoustic signals, but with previous knowledge of reference signals and source positions. The problem of finding the sensor position is stated as a function of estimated times-of-flight (TOFs) or time-differences-of-flight (TDOFs) from the sound sources to the target microphone, and the main practical issues involved in TOF estimation are discussed. Least-squares ASL solutions are introduced, followed by other strategies inspired by sound source localization solutions: steered-response power, which improves localization accuracy, and a new region-based search, which alleviates complexity. A set of complementary techniques for further improvement of TOF/TDOF estimates are reviewed: sliding windows, matching pursuit, and TOF selection. The paper proceeds with proposing a novel ASL method that combines most of the previous material, whose performance is assessed in a real-world example: in a typical lecture room, the method achieves accuracy better than 20 cm.

Whenever an audio device introduces unwanted nonlinear distortions into the manipulated signal, finding a tractable system to approximately model and estimate such degradations can be instrumental to recover the undistorted audio. This paper approaches such blind estimation task (for which classical identification tools are unsuitable) by bringing into the Bayesian framework a Hammerstein system model: the cascade of a static memoryless nonlinearity with a memory-inducing linear filter, which has been shown to be effective in describing many real systems. By assuming the underlying clean audio signal is autoregressive in short sections, the proposed method identifies the distorting system by simulating, in a Markov-Chain Monte Carlo context, the posterior distribution of the model parameters conditioned on the distorted signal. To deal with the resulting non-standard posterior distribution, a combination of the Metropolis-Hastings (MH) algorithm and the Gibbs Sampling is adopted. MH proposals are based on the Laplace approximation of the posterior distribution thanks to its almost Gaussian shape around modes. A heuristic that forces a broad region of the parameter space to be visited on an occasional basis prevents the Markov Chain from getting stuck around local maxima. A series of experiments with artificially distorted music recordings attests the effectiveness of the proposed algorithm.

Self-localization of smart portable devices serves as foundation for several novel applications. This work proposes a set of algorithms that enable a mobile device to passively determine its position relative to a known reference with centimeter precision, based exclusively on the capture of acoustic signals emitted by controlled sources around it. The proposed techniques tackle typical practical issues such as reverberation, unknown speed of sound, line-of-sight obstruction, clock skew, and the need for asynchronous operation. After their theoretical developments and off-line simulations, the methods are assessed as real-time applications embedded into off-the-shelf mobile devices operating in real scenarios. When line of sight is available, position estimation errors are at most 4 cm using recorded signals.

This letter proposes an efficient method based on the steered-response power (SRP) technique for sound source localization using microphone arrays: the refined volumetric SRP (RV-SRP). By deploying a sparser volumetric grid, the RV-SRP achieves a significant reduction of the computational complexity without sacrificing the accuracy of location estimates. In addition, a refinement step improves on the compromise between complexity and accuracy. Experiments conducted in both simulated- and real-data scenarios show that the RV-SRP outperforms state-of-the-art methods in accuracy with lower computational cost.

Many real-world signals are subjected to nonlinear distortions that can be approximately modeled as memoryless and invertible. In Audio applications, they are typical of magnetic recordings but can also result of dynamic compression employed in vinyl recordings etc. Such an effect can be disturbing to a modern audience which is used to higher quality material. This paper proposes an iterative algorithm to maximize the likelihood function of the distortion function parameters, based solely on samples of the degraded signal, and then recover the original signal. The method assumes the original signal to be autoregressive and Gaussian in short sections - a standard model for audio - and the nonlinearity to be time-invariant throughout the signal, thus allowing the use of all samples in the model estimation. Additionally, a simple and time-efficient alternative technique to estimate the nonlinear function is proposed; it can be used either as a fast and reliable stand-alone procedure or as a initialization routine for the more sophisticated maximum likelihood approach. The robustness of the proposed techniques is verified through application to artificial and real signals nonlinearly distorted.

In this paper, an edit detection method for forensic audio analysis is proposed. It develops and improves a previous method through changes in the signal processing chain and a novel detection criterion. As with the original method, electrical network frequency (ENF) analysis is central to the novel edit detector, for it allows monitoring anomalous variations of the ENF related to audio edit events. Working in unsupervised manner, the edit detector compares the extent of ENF variations, centered at its nominal frequency, with a variable threshold that defines the upper limit for normal variations observed in unedited signals. The ENF variations caused by edits in the signal are likely to exceed the threshold providing a mechanism for their detection. The proposed method is evaluated in both qualitative and quantitative terms via two distinct annotated databases. Results are reported for originally noisy database signals as well as versions of them further degraded under controlled conditions. A comparative performance evaluation, in terms of equal error rate (EER) detection, reveals that, for one of the tested databases, an improvement from 7% to 4% EER is achieved, respectively, from the original to the new edit detection method. When the signals are amplitude clipped or corrupted by broadband background noise, the performance figures of the novel method follow the same profile of those of the original method.

Impulsive noise, also known as clicks, is a very common type of distortion in old gramophone recordings. Existing methods (both heuristic and statistical) for removal of this type of defect usually do not exploit its underlying physical generation. This work proposes a model in which each click is individually modeled, which is more physically meaningful. A Bayesian method based on the reversible-jump Metropolis-Hastings algorithm for joint detection and removal of impulsive noise in audio signals is devised. Simulations with artificial and real audio signals as well as comparisons with competing approaches are presented to illustrate and validate the proposed method.

This paper discusses the automatic quality assessment of echo-degraded speech in the context of teleconference systems. Subjective listening tests conducted over a carefully designed database of signals degraded by acoustic echo have been used to assess how this impairment is perceived and to determine which parameters have a significant impact on speech quality. The results have shown that, similarly to electric transmission line echo, acoustic echo is mainly influenced by echo delay and echo gain. Based on this observation, a mapping between these two parameters and the mean subjective score is devised. Moreover, a signal-based algorithm for the estimation of these parameters is described, and its performance is evaluated. The complete system comprising both the parameter estimators and the mapping function achieves a correlation of 94% between predicted and actual subjective scores, and can be employed as a non-intrusive monitoring tool for in-service quality evaluation of teleconference systems. Further validation indicates the operating range of the proposed quality assessment tool can be extended by proper retraining.

This paper addresses the problem of identifying impairment types that might be present in a speech signal. In particular, three acoustically induced degradation types that occur in teleconference systems are considered: acoustic echo, reverberation, and broadband noise, as well as combinations among them. The proposed system is double-ended (full reference) and is developed using a database of degraded full-band speech signals created according to a model for teleconference systems. A set of features obtained from both the degraded and non-degraded signals is proposed and shown to adequately capture information associated with each degradation type. A random forest classifier and a support vector machine are successfully employed, achieving a classification error below 2%. Such classifiers can be used to select an appropriate quality assessment tool for a given degraded signal.

This paper addresses a forensic tool used to assess audio authenticity. The proposed method is based on detecting phase discontinuity of the power grid signal; this signal, referred to as electric network frequency (ENF), is sometimes embedded in audio signals when the recording is carried out with the equipment connected to an electrical outlet or when certain microphones are in an ENF magnetic field. After down-sampling and band-filtering the audio around the nominal value of the ENF, the result can be considered a single tone such that a high-precision Fourier analysis can be used to estimate its phase. The estimated phase provides a visual aid to locating editing points (signalled by abrupt phase changes) and inferring the type of audio editing (insertion or removal of audio segments). From the estimated values, a feature is used to quantify the discontinuity of the ENF phase, allowing an automatic decision concerning the authenticity of the audio evidence. The theoretical background is presented along with practical implementation issues related to the proposed technique, whose performance is evaluated on digitally edited audio signals.

Modern telepresence systems can deliver multimedia signals of unprecedentedly high quality of experience to the user. Setting and maintaining such services call for reliable and automatic tools for multimedia quality probing, in special those targeted at speech data along the transmission path. Most of the objective methods for sound quality assessment (QA) in the literature are intended for either speech signals of 4- to 8-kHz bandwidth or general audio until 24 kHz, but are not specifically designed for speech at high sampling-rates. This work approaches quality evaluation of full-band (24 kHz) high-quality speech corrupted by echo. A simple metric singled out from a standardized double-ended tool for audio QA is proposed as a solution for the problem at hand. Quality measures from a set of speech stimuli corrupted by echo under controlled conditions were obtained via listening tests to allow calibration and evaluation of the proposed method. Experimental results reveal an overall correlation of 0.94 between objective and subjective scores, even in the presence of moderate additive noise.

In this paper the design of a double-ended (intrusive) diagnostic tool for identifying five types of degradation in audio signals is reported. The impairment types taken into consideration are additive contamination with pink noise, occurrences of signal mutes, distortion by magnitude clipping, and the previous two types mixed with pink noise. As a simple solution to accomplish the established goal, a threshold-based hierarchical classification system is proposed, being completely defined from pre-processing of the input signals, passing through the estimation of a few characteristic features, up to data clustering criteria. Performance evaluation of the classifier is carried out via a validation database containing 60 impaired signals for each type of impairment, with five distinct degradation intensity levels. Considering the types and range of degradation levels considered in this work, excellent results are achieved, scoring above 96% of correctly classified data in the worst case. System performance in identifying mixed impairment types tends to deteriorate as the strength of the noise component increases.

This paper constitutes an introduction to the field of quality evaluation of sound (speech and audio) signals. The need for such an assessment is inherent to modern communications: VoIP, mobile phone, or teleconference systems require meaningful measures of performance, which may ultimately assure good service or profitable business. A brief survey on subjective and objective evaluation methods is provided. Recent developments as well as new topics to be investigated are also addressed. Experiments are conducted to illustrate how to validate quality assessment methods.

Classic methods for sinusoidal analysis rely on partial tracking, a technique where successive sets of spectral peaks of an audio signal must be properly associated in time. The resulting tracks describe, in terms of amplitude and frequency, the continuous evolution of the so-called partials which, combined, model the complex sounds emitted by a given instrument. A well-known challenge in this context is preserving amplitude and frequency coherence in the tracking mechanism, specially in cases where failure in peak detection may occur, or perhaps in the event of crossing partials. This paper presents a new decision-directed recursive least-squares (RLS) estimation method for frequency and amplitude tracking in sinusoidal analysis. Different performance measurements show that the proposed deterministic algorithm outperforms some procedures currently found in the literature.

Under anechoic conditions, a system that generates 3D binaural sound should simulate the head-related transfer functions (HRTFs) that represent sound changes from the source to the listener's ears. Such a system could directly use HRTFs measured over a sphere around the listener. However, since it is not feasible to perform measurements for every position, practical systems interpolate the necessary functions between measured HRTFs. This paper proposes a generalization of the continuous variable digital delay (CVDD) described in the literature, yielding a two spatial-variable dependent transfer function. It performs a polynomial interpolation whose coefficients are optimized on a region over the sphere, so that the angular coordinates of any point inside the region map directly into the corresponding HRTFs. Performance assessment is done through graphical means. The issue of continuity in moving sound generation is also approached, through the combination of adjacent-region structures

This paper presents an efficient model-based interpolator for reconstructing long portions of missing samples in audio signals. A simple modification to an existing interpolation method is proposed, optimizing the balance between computational requirements and qualitative performance. Moreover, a post-processing multirate interpolation scheme is introduced to further enhance the quality of the restored signals. Subjective quality assessments of the reconstructed signals via listening tests substantiate the improved performance of the proposed algorithm

The paper proposes a new transform for musical audio signals. The new transform, so-called modified constant-Q fast filter bank (mCQFFB), yields a log-like description in the frequency domain with improved frequency response when compared to the standard DFT. The improved frequency response of the mCQFFB is achieved by interpreting the sliding-DFT (sDFT) as a multi-stage filter bank, and substituting the sDFT basic filter in this description by a higher-order filter with improved characteristics. The logarithmic spectral scale of the mCQFFB is achieved by the adequate resampling of a chosen channel filter. We also discuss implementation issues related to the mCQFFB and exemplify its application to the analysis of musical signals.

This letter investigates the deviation of least squares AR-model estimates induced by linear quantization of the observable signal under modeling. Model correction is attained by describing the undesirable bias as a function of the number of bits used to represent the signal. Sensitivity considerations are employed to explain the behavior of the deviations on the generator filter poles. Numerical examples are included.

This paper addresses the interpolation of Head-Related Transfer Functions (HRTFs) for 3D-sound generation through headphones. HRTFs are transfer functions associated with the paths between sound sources and the ears, and are usually measured for a finite set of source locations around the listener. Generation of 3D-sound at any other virtual positions requires interpolation procedures. In this work, the Inter-positional Transfer Function (IPTF) is introduced and an IPTF-based new method for HRTF interpolation is proposed. Comparisons between the IPTF-based and the bilinear interpolation methods are provided, with focus on interpolation accuracy and computational complexity.

This work presents analytical expressions as well as related curves to determine the maximum magnitude and phase deviations caused by a zero-pole pair, an individual pole or an individual zero on a discrete-time transfer function. The main motivation is to provide a clear link between the position of zeros and poles, considering their magnitudes and phases, and the effects of discarding them from a given transfer function, since evaluating these effects is crucial to attain the order reduction of physical system models.

This work proposes a model, originated from the power spectral density concept, for the sub-band processes obtained by analysis of an ARMA process by a decimating filter bank. As an example, the model for an AR process analyzed in octaves by a tree-structured FIR filter bank is derived in a recursive way, and some interpretations are given.

An efficient approach for the computation of the optimum convergence factor for the LMS (least mean square)/Newton algorithm applied to a transversal FIR structure is proposed. The approach leads to a variable step size algorithm that results in a dramatic reduction in convergence time. The algorithm is evaluated in systems identification applications.>