Presentation attack detection (PAD) aims to determine if a speech signal observed at a microphone was produced by a live talker or if it was replayed through a loudspeaker. This is an important problem to address for secure human-computer voice interactions. One characteristic of presentation attacks where recording and replay occur within enclosed reverberant environments is that the observed speech in a live-talker scenario will undergo one acoustic impulse response (AIR) while there will be a pair of convolved AIRs in the replay scenario. We investigate how this physical fact may be used to detect a presentation attack. Drawing on established results in room acoustics, we show that the spectral standard deviation of an AIR is a promising feature for distinguishing between live and replayed speech. We develop a method based on convolutional neural networks (CNNs) to estimate the spectral standard deviation directly from a speech signal, leading to a zero-shot PAD approach. Several aspects of the detectability based on room acoustics alone are illustrated using data from ASVspoof2019 and ASVspoof2021.

We present a performance comparison between human listeners and a simple algorithm for the task of speech anomaly detection. The algorithm utilises an intentionally small set of features derived from the source-filter model, with the aim of validating that key components of source-filter theory characterise how humans perceive anomalies. We furthermore recognise that humans are adept at detecting anomalies without prior exposure to a given anomaly class. To that end, we also consider the algorithm performance when operating via the principle of unsupervised learning where a null model is derived from normal speech recordings. We evaluate both the algorithm and human listeners for pitch-shift detection where the pitch of a speech sample is intentionally modified using software, a phenomenon of relevance to the fields of fraud detection and forensics. Our results show that humans can only detect pitch-shift reliably at more extreme levels, and that the performance of the algorithm matches closely with that of humans.

Key parameters that characterise the degree of degradation in an acoustic environment are reverberation time (RT), direct-to-reverberant ratio (DRR) and signal-to-noise ratio (SNR). To address the inherent interplay that exists between these parameters, which can hinder existing methods designed to estimate only a single parameter, we propose a data-driven solution to jointly estimate all three parameters using a convolutional neural network. To facilitate robustness to unseen acoustic conditions, the method is trained using a large set of simulated acoustic impulse responses that have been carefully selected so as to mitigate interplays that exist between RT and DRR. In this work we evaluate the performance of the proposed estimator with respect to reverberation only. Results show the estimator compares favourably with respect to the state-of-the-art for unseen and real acoustic scenarios.

Indoor localisation is an important research topic with several possible applications. For example, knowing a user's location can be used as navigation aid in hospitals and malls, or for better targeted marketing. In this paper we consider the case where the environment of interest is equipped with several receivers (with known location) from which time-difference-of-arrival (TDOA) measurements are obtained and used to localise the source. We will present a distributed algorithm for localising the source. More specifically, we experimentally show that the distributed algorithm, which only uses time-of-arrival (TOA) measurements obtained from neighbouring receivers to calculate the TDOAs, performs as well as a centralised solution that has access to all TOA measurements in the network. In addition, we propose a method for discarding erroneous TOA measurements which considerably improves the performance in noisy and reverberant environments.

A computer being able to estimate the geometry of a room could benefit applications such as auralization, robot navigation, virtual reality and teleconferencing. When estimating the geometry of a room using multiple microphones, the main challenge is to identify which reflections, or echoes, originate from the same wall and can, therefore, be modeled by a virtual source outside the room using the mirror image source model. In this paper we present a new and efficient method to disambiguate the echoes using a graph theoretical approach where echo combinations are modeled as nodes in a graph and the problem is stated as a maximum independent set problem. Once the echoes are correctly labelled, we know the locations of the virtual sources from which we can infer the room geometry. Experiments for shoe-box shaped rooms show that we can reliably estimate the room geometry within seconds on contemporary hardware and achieve centimeter precision on finding the vertices of the room.

Reverberation time (T60) and Direct-to-reverberant ratio (DRR) are important parameters which together can characterize sound captured by microphones in nonanechoic rooms. These parameters are important in speech processing applications such as speech recognition and dereverberation. The values of T60and DRR can be estimated directly from the acoustic impulse response (AIR) of the room. In practice, the AIR is not normally available, in which case these parameters must be estimated blindly from the observed speech in the microphone signal. The acoustic characterization of environments (ACE) challenge aimed to determine the state-of-the-art in blind acoustic parameter estimation and also to stimulate research in this area. A summary of the ACE challenge, and the corpus used in the challenge is presented together with an analysis of the results. Existing algorithms were submitted alongside novel contributions, the comparative results for which are presented in this paper. The challenge showed that T60estimation is a mature field where analytical approaches dominate whilst DRR estimation is a less mature field where machine learning approaches are currently more successful.

We propose a fast speech analysis method which simultaneously performs high-resolution voiced/unvoiced detection (VUD) and accurate estimation of glottal closure and glottal opening instants (GCIs and GOIs, respectively). The proposed algorithm exploits the structure of the glottal flow derivative in order to estimate GCIs and GOIs only in voiced speech using simple time-domain criteria. We compare our method with well-known GCI/GOI methods, namely, the dynamic programming projected phase-slope algorithm (DYPSA), the yet another GCI/GOI algorithm (YAGA) and the speech event detection using the residual excitation and a mean-based signal (SEDREAMS). Furthermore, we examine the performance of the aforementioned methods when combined with state-of-the-art VUD algorithms, namely, the robust algorithm for pitch tracking (RAPT) and the summation of residual harmonics (SRH). Experiments conducted on the APLAWD and SAM databases show that the proposed algorithm outperforms the state-of-the-art combinations of VUD and GCI/GOI algorithms with respect to almost all evaluation criteria for clean speech. Experiments on speech contaminated with several noise types (white Gaussian, babble, and car-interior) are also presented and discussed. The proposed algorithm outperforms the state-of-the-art combinations in most evaluation criteria for signal-to-noise ratio greater than 10 dB.

In this paper, a broadband region-based near-field beamforming algorithm is proposed and demonstrated for acoustic applications. We use an eigenfilter structure with a minimum-energy cost function based on desired and undesired near-field regions. Robustness is thus achieved by focusing on signals generated from desired zones in space while rejecting signals from undesired zones. This construction leads to a linear matrix pencil formulated in terms of the array gain to these desired and undesired zones. We include a far-field model as part of the rejection zones that further improves performance in reverberant environments. We demonstrate the robustness of the algorithm in simulated and real scenarios.

In wireless acoustic sensor networks (WASNs), clock synchronization is crucial for multi-microphone signal processing, since clock differences between capturing devices will cause signal drift. This in turn severely degrades the performance of multi-microphone signal processing. After a theoretical analysis of the effect of clock synchronization, we evaluate the use of three different clock synchronization algorithms in the context of multi-microphone noise reduction. Our experimental study shows that the achieved precision of clock synchronization enables sufficient accuracy of clock synchronization for the MVDR beamformer in ideal scenarios. However, in practical scenarios with measurement noise on the parameters of interest, time-stamp based clock synchronization algorithms get degraded, while signal based algorithms are still accurate enough for the MVDR beamformer, albeit at a much higher transmission cost.

We present a method for calibrating a distributed microphone array using time-of-arrival (TOA) measurements. The calibration encompasses localization and gain equalization of the microphones, which are both important in applications such as beamforming. The availability of accurate TOA measurements between the microphones and a set of spatially distributed acoustic events is pivotal to the calibration task. We propose to use a moving acoustic source emitting a calibration signal at known intervals. We then show that the TOAs and the observed signals can be used to estimate the gain differences between microphones in addition to the more established microphone localization. Finally, we provide experimental results with simulated and real measured data to demonstrate that our approach facilitates accurate TOA measurements and hence, accurate localization and gain equalization, even in reverberant and noisy conditions.

In many applications using multiple sensors, knowledge of the relative positions of the sensors is required. The locations of the sensors can be obtained from measured time-of-arrivals (TOAs) of events generated by sources. Although several TOA-based localization techniques exist, practical TOA measurements are incomplete because they include an unknown internal delay; the time taken from the signal reaching the sensor to that it is registered as received by the capturing device. In order to localize the sensors properly, these internal delays need to be estimated accurately. In this paper we propose a method for estimating the internal delays by using a data fitting technique based on structured total least squares. Under reasonable assumptions we show that the algorithm is guaranteed to converge to the optimal solution and ultimately achieves a quadratic rate of convergence. Experimental results show that the execution time is less than 1% of the execution time of existing methods while attaining an even higher accuracy.

Reverberation Time (T60) is an important measure of the acoustic properties of a room. It can provide information about the acoustic environment, the intelligibility, and quality of speech recorded in the room, and help improve the performance of speech processing algorithms with reverberant speech. Where the acoustic impulse response of the room is not available, the T60must be estimated non-intrusively from reverberant speech. State-of-the-art non-intrusive T60estimators have been shown to be strongly biased in the presence of noise. We describe a novel T60estimation algorithm based on spectral decay distributions that provides robustness to additive noise for a range of realistic noise types for signal-to-noise ratios in the range 0 to 35 dB and T60s between 200 and 950 ms. The proposed method also has much reduced computational cost.

We present a method for automatic microphone localization in adhoc microphone arrays. The localization is based on time-of-arrival (TOA) measurements obtained from spatially distributed acoustic events. In practice, measured TOAs are an incomplete representation of the true TOAs due to unknown onset times of the acoustic events and internal delays in the capturing devices and make the localization problem insoluble if not addressed appropriately. The main contribution of the proposed method is an algorithm that identifies and corrects for such internal delays and acoustic event onset times in the measured TOAs. Experimental results using both simulated and real-world data demonstrate the performance of the method and highlight the significance of correct estimation of the internal delays and onset times.

We present an algorithm for blind estimation of the magnitude response of an acoustic channel from single microphone observations of a speech signal. The algorithm employs channel robust RASTA filtered Mel-frequency cepstral coefficients as features to train a Gaussian mixture model based classifier and average clean speech spectra are associated with each mixture; these are then used to blindly estimate the acoustic channel magnitude response from speech that has undergone spectral modification due to the channel. Experimental results using a variety of simulated and measured acoustic channels and additive babble noise, car noise and white Gaussian noise are presented. The results demonstrate that the proposed method is able to estimate a variety of channel magnitude responses to within an Itakura distance of dI ≤0.5 for SNR ≥10 dB.

We present a non-intrusive data driven method for codec detection and identification in the presence of background noise. The method uses a number of speech features which are then used to train a CART classifier. We demonstrate the performance of the method using several different noise types over a wide range of SNRs. Our results show that we can identify a codec and its bit rate to an accuracy of 92% and we are able to detect the presence of a codec with an accuracy of 97% at -5 dB SNR.

The effect of additive sensor noise on single-input-multiple-output (SIMO) blind system identification (BSI) algorithms based upon cross-relation (CR) error is investigated. Previous studies have shown that additive noise in the observed signal results in systems comprising the true estimated channels convolved with an erroneous ‘common filter’, and additionally that identification and removal of this filter significantly improves estimation error. However, the source of the common filter remained an open question. This paper explains the common filter through a first-order perturbation analysis of the CR matrix, showing that it be estimated from the perturbation and the eigenvectors of the noiseless CR matrix. The analysis given in this paper provides a new insight into the effect of noise on SIMO BSI algorithms and forms the first step towards an overall noise robust solution.

This paper presents the development of a compact vocabulary for describing the audible characteristics of degraded speech. An experiment was conducted with 51 English-speaking subjects who were tasked with assigning one of a list of given text descriptors to 220 degradation conditions. Exploratory data analysis using hierarchical clustering resulted in a compact vocabulary of 10 classes, which was further validated by a bootstrap cluster analysis.

Equalization of room transfer functions (RTFs) is an important topic with several applications in acoustic signal processing. RTFs are often modeled as finite-impulse response filters, characterized by orders of thousands of taps and non-minimum phase. In practice, only approximate estimates of the actual RTFs are available due to measurement noise, limited estimation accuracy, and temporal variation of source-receiver position. These issues make equalization a difficult problem. In this paper, we discuss multichannel equalization with focus on inexact RTF estimates. We present a multichannel method for the equalization filter design utilizing decimated and oversampled subbands, where the full-band acoustic impulse response is decomposed into equivalent subband filters prior to equalization. This technique is not only more computationally efficient but also more robust to impulse response inaccuracies compared with the full-band counterpart.

Reverberant speech can be described as sounding distant with noticeable coloration and echo. These detrimental perceptual effects are caused by early and late reflections, respectively, and reduces the fidelity and intelligibility of speech. It is well-known that the echo density of the reflections increases with time. Therefore, the temporal structure of early and late reflections differs. In this paper, we combine two different dereverberation techniques that were recently developed to suppress early and late reverberation separately. First, late reverberation is suppressed using a spectral processing technique that is based on a statistical reverberation model. Secondly, early reverberation and residual late reverberation are suppressed using a linear prediction (LP) residual processing technique. In addition, an objective measure based on the kurtosis of the LP residual is proposed to measure the coloration caused by early reflections. Experimental results demonstrate the beneficial use of the new single microphone system that reduces echo and coloration with little speech distortion.

Equalization techniques for room impulse responses (RIRs) are important in acoustic signal processing applications such as speech dereverberation. In practice, only approximate estimates of the RIRs are available and the inverse filters designed from these estimates may cause significant distortion in the equalized signal. A second issue is that existing equalizer design algorithms are computationally expensive. We here propose regularized subband equalizer design algorithm. Both the computational complexity and the robustness of the equalizer design to system estimation errors are improved. An analysis of the computational complexity and simulation examples are provided to support our study.

Speech signals acquired in a reverberant room with microphones positioned at a distance from the talker are degraded in quality due to reverberation and measurement noise. Therefore, enhancement of reverberant speech is important in hands-free telecommunications applications. The perceptual effects of reverberation can be linked to the room impulse response (RIR) between the talker and the microphone and are characterized by: (i) colouration, due to the strong early reflections and (ii) a distant 'echoey' quality due to the decaying tail of the RIR. Accordingly, we present a two-stage multimicrophone method for speech dereverberation. First, spatiotemporal averaging is performed on the linear prediction residual, which primarily reduces the effects of the early reflections. Secondly, a spectral subtraction method is employed to reduce late reverberation. Simulation results with measured RIRs and additive white Gaussian noise illustrate the performance of this method and show that the combined approach performs better than each of the two stages individually.

A class of adaptive blind channel identification algorithms were proposed recently and were demonstrated to be able to successfully identify various types of channels when the observed signals are free from significant levels of measurement noise. In this paper, we provide a study of the effects of noise on these algorithms and show that they misconverge even at moderate values of SNR. We introduce a spectral constraint into the adaptation rule and show that the robustness to noise can be considerably improved. Simulation results are presented for the new algorithm, which demonstrate a significant performance improvement in terms of normalized projection misalignment

The choice of step-size in adaptive blind channel identification using the multichannel least mean squares (MCLMS) algorithm is critical and controls its convergence rate, stability, and sensitivity to noise. In this letter, we derive the expression for an optimal step-size in the Wiener sense and investigate its properties. An implementation technique for the Wiener solution of the self-adaptive step-size is presented, and it is shown that significant performance improvements are obtained compared to existing approaches in the presence of noise