In many distant speech acquisition scenarios, such as hands-free telephony or teleconferencing, the desired speech signal is corrupted by noise and reverberation. This degrades both the speech quality and intelligibility, making communication difficult or even impossible. Speech enhancement techniques seek to mitigate these effects and extract the desired speech signal. This objective is commonly achieved through the use of microphone arrays, which take advantage of the spatial properties of the sound field in order to reduce noise and reverberation. Spherical microphone arrays, where the microphones are arranged in a spherical configuration, usually mounted on a rigid baffle, are able to analyze the sound field in three dimensions; the captured sound field can then be efficiently described in the spherical harmonic domain (SHD). In this thesis, a number of novel spherical array processing algorithms are proposed, based in the SHD. In ...

This thesis deals with the efficient and flexible acquisition and processing of spatial sound using multiple microphones. In spatial sound acquisition and processing, we use multiple microphones to capture the sound of multiple sources being simultaneously active at a rever- berant recording side and process the sound depending on the application at the application side. Typical applications include source extraction, immersive spatial sound reproduction, or speech enhancement. A flexible sound acquisition and processing means that we can capture the sound with almost arbitrary microphone configurations without constraining the application at the ap- plication side. This means that we can realize and adjust the different applications indepen- dently of the microphone configuration used at the recording side. For example in spatial sound reproduction, where we aim at reproducing the sound such that the listener perceives the same impression as if he ...

Audio systems receive the speech signals of interest usually in the presence of noise. The noise has profound impacts on the quality and intelligibility of the speech signals, and it is therefore clear that the noisy signals must be cleaned up before being played back, stored, or analyzed. We can estimate the speech signal of interest from the noisy signals using a priori knowledge about it. A human speech signal is broadband and consists of both voiced and unvoiced parts. The voiced part is quasi-periodic with a time-varying fundamental frequency (or pitch as it is commonly referred to). We consider the periodic signals basically as the sum of harmonics. Therefore, we can pass the noisy signals through bandpass filters centered at the frequencies of the harmonics to enhance the signal. In addition, although the frequencies of the harmonics are the ...

A speech signal captured by multiple microphones is often subject to a reduced intelligibility and quality due to the presence of noise and room acoustic interferences. Multi-microphone speech enhancement systems therefore aim at the suppression or cancellation of such undesired signals without substantial distortion of the speech signal. A fundamental aspect to the design of several multi-microphone speech enhancement systems is that of the spatial information which relates each microphone signal to the desired speech source. This spatial information is unknown in practice and has to be somehow estimated. Under certain conditions, however, the estimated spatial information can be inaccurate, which subsequently degrades the performance of a multi-microphone speech enhancement system. This doctoral dissertation is focused on the development and evaluation of acoustic signal processing algorithms in order to address this issue. Specifically, as opposed to conventional means of estimating ...

Reverberation is the sum of reflected sound waves and is present in any conventional room. Speech communication devices such as mobile phones in hands-free mode, tablets, smart TVs, teleconferencing systems, hearing aids, voice-controlled systems, etc. use one or more microphones to pick up the desired speech signals. When the microphones are not in the proximity of the desired source, strong reverberation and noise can degrade the signal quality at the microphones and can impair the intelligibility and the performance of automatic speech recognizers. Therefore, it is a highly demanded task to process the microphone signals such that reverberation and noise are reduced. The process of reducing or removing reverberation from recorded signals is called dereverberation. As dereverberation is usually a completely blind problem, where the only available information are the microphone signals, and as the acoustic scenario can be non-stationary, ...

The problem of segregating a sound source of interest from an acoustic background has been extensively studied due to applications in hearing prostheses, robust speech/speaker recognition and audio information retrieval. Computational auditory scene analysis (CASA) approaches the segregation problem by utilizing grouping cues involved in the perceptual organization of sound by human listeners. Binaural processing, where input signals resemble those that enter the two ears, is of particular interest in the CASA field. The dominant approach to binaural segregation has been to derive spatially selective filters in order to enhance the signal in a direction of interest. As such, the problems of sound localization and sound segregation are closely tied. While spatial filtering has been widely utilized, substantial performance degradation is incurred in reverberant environments and more fundamentally, segregation cannot be performed without sufficient spatial separation between sources. This dissertation ...

The topic of this thesis is the enhancement of noisy, periodic signals with application to speech signals. Generally speaking, enhancement methods can be divided into signal- and noise-driven methods. In this thesis, we focus on the signal-driven approach by employing relevant signal parameters for the enhancement of periodic signals. The enhancement problem consists of two major subproblems: the estimation of relevant parameters or statistics, and the actual noise reduction of the observed signal. We consider both of these subproblems. First, we consider the problem of estimating signal parameters relevant to the enhancement of periodic signals. The fundamental frequency is one example of such a parameter. Furthermore, in multichannel scenarios, the direction-of-arrival of the periodic sources onto an array of sensors is another parameter of relevance. We propose methods for the estimation of the fundamental frequency that have benefits compared to ...

This dissertation focuses on the development of novel parametric spatial audio techniques using compact microphone arrays. Compact arrays are of special interest since they can be adapted to fit in portable devices, opening the possibility of exploiting the potential of immersive spatial audio algorithms in our daily lives. The techniques developed in this thesis consider the use of signal processing algorithms adapted for human listeners, thus exploiting the capabilities and limitations of human spatial hearing. The findings of this research are in the following three areas of spatial audio processing: directional filtering, spatial audio reproduction, and direction of arrival estimation. In directional filtering, two novel algorithms have been developed based on the cross-pattern coherence (CroPaC). The method essentially exploits the directional response of two different types of beamformers by using their cross-spectrum to estimate a soft masker. The soft masker ...

Recent advances in computer technology and speech and language processing have made possible that some new ways of person-machine communication and computer assistance to human activities start to appear feasible. Concretely, the interest on the development of new challenging applications in indoor environments equipped with multiple multimodal sensors, also known as smart-rooms, has considerably grown. In general, it is well-known that the quality of speech signals captured by microphones that can be located several meters away from the speakers is severely distorted by acoustic noise and room reverberation. In the context of the development of hands-free speech applications in smart-room environments, the use of obtrusive sensors like close-talking microphones is usually not allowed, and consequently, speech technologies must operate on the basis of distant-talking recordings. In such conditions, speech technologies that usually perform reasonably well in free of noise and ...

In direction finding (DF) applications, there are several factors affecting the estimation accuracy of the direction-of-arrivals (DOA) of unknown source locations. The major distortions in the estimation process are due to the array imperfections, model mismatches and multipath. The array imperfections usually exist in practical applications due to the nonidealities in the antenna array such as mutual coupling (MC) and gain/phase uncertainties. The model mismatches usually occur when the model of the received signal differs from the signal model used in the processing stage of the DF system. Another distortion is due to multipath signals. In the multipath scenario, the antenna array receives the transmitted signal from more than one path with different directions and the array covariance matrix is rank-deficient. In this thesis, three new methods are proposed for the problems in DF applications in the presence of array ...

In the modern world, we are increasingly surrounded by computation devices with communication links and one or more microphones. Such devices are, for example, smartphones, tablets, laptops or hearing aids. These devices can work together as nodes in an acoustic sensor network (ASN). Such networks are a growing platform that opens the possibility for many practical applications. ASN based speech enhancement, source localization, and event detection can be applied for teleconferencing, camera control, automation, or assisted living. For this kind of applications, the awareness of auditory objects and their spatial positioning are key properties. In order to provide these two kinds of information, novel methods have been developed in this thesis. Information on the type of auditory objects is provided by a novel real-time sound classification method. Information on the position of human speakers is provided by a novel localization ...

Sound source localization algorithms have a long research history in the field of digital signal processing. Many common applications like intelligent personal assistants, teleconferencing systems and methods for technical diagnosis in acoustics require an accurate localization of sound sources in the environment. However, dynamic environments entail a particular challenge for these systems. For instance, voice controlled smart home applications, where the speaker, as well as potential noise sources, are moving within the room, are a typical example of dynamic environments. Classical sound source localization systems only have limited capabilities to deal with dynamic acoustic scenarios. In this thesis, three novel approaches to sound source localization that extend existing classical methods will be presented. The first system is proposed in the context of audiovisual source localization. Determining the position of sound sources in adverse acoustic conditions can be improved by including ...

In modern devices which provide hands-free speech capturing functionality, such as hands-free communication kits and voice-controlled devices, the received speech signal at the microphones is corrupted by background noise, interfering speech signals, and room reverberation. In many practical situations, the microphones are not necessarily located near the desired source, and hence, the ratio of the desired speech power to the power of the background noise, the interfering speech, and the reverberation at the microphones can be very low, often around or even below 0 dB. In such situations, the comfort of human-to-human communication, as well as the accuracy of automatic speech recognisers for voice-controlled applications can be signi cantly degraded. Therefore, e ffective speech enhancement algorithms are required to process the microphone signals before transmitting them to the far-end side for communication, or before feeding them into a speech recognition ...

Despite the outstanding progress made on automatic speech recognition (ASR) throughout the last decades, noise-robust ASR still poses a challenge. Tackling with acoustic noise in ASR systems is more important than ever before for a twofold reason: 1) ASR technology has begun to be extensively integrated in intelligent mobile devices (IMDs) such as smartphones to easily accomplish different tasks (e.g. search-by-voice), and 2) IMDs can be used anywhere at any time, that is, under many different acoustic (noisy) conditions. On the other hand, with the aim of enhancing noisy speech, IMDs have begun to embed small microphone arrays, i.e. microphone arrays comprised of a few sensors close each other. These multi-sensor IMDs often embed one microphone (usually at their rear) intended to capture the acoustic environment more than the speaker’s voice. This is the so-called secondary microphone. While classical microphone ...

This dissertation details three approaches for direction-of-arrival (DOA) estimation or beamforming in array signal processing from the perspective of sparsity. In the first part of this dissertation, we consider sparse array beamformer design based on the alternating direction method of multipliers (ADMM); in the second part of this dissertation, the problem of joint DOA estimation and distorted sensor detection is investigated; and off-grid DOA estimation is studied in the last part of this dissertation. In the first part of this thesis, we devise a sparse array design algorithm for adaptive beamforming. Our strategy is based on finding a sparse beamformer weight to maximize the output signal-to-interference-plus-noise ratio (SINR). The proposed method utilizes ADMM, and admits closed-form solutions at each ADMM iteration. The algorithm convergence properties are analyzed by showing the monotonicity and boundedness of the augmented Lagrangian function. In addition, ...