Independent Component Analysis.

In speech communication systems, such as voice-controlled systems, hands-free mobile telephones, and hearing aids, the received microphone signals are degraded by room reverberation, background noise, and other interferences This signal degradation may lead to total unintelligibility of the speech and decreases the performance of automatic speech recognition systems In the context of this work reverberation is the process of multi-path propagation of an acoustic sound from its source to one or more microphones The received microphone signal generally consists of a direct sound, reflections that arrive shortly after the direct sound (commonly called early reverberation), and reflections that arrive after the early reverberation (commonly called late reverberation) Reverberant speech can be described as sounding distant with noticeable echo and colouration These detrimental perceptual effects are primarily caused by late reverberation, and generally increase with increasing distance between the source and microphone Conversely, early reverberations tend to improve the intelligibility of speech In combination with the direct sound it is sometimes referred to as the early speech component Reduction of the detrimental effects of reflections is evidently of considerable practical importance, and is the focus of this dissertation More specifically the dissertation deals with dereverberation techniques, ie, signal processing techniques to reduce the detrimental effects of reflections In the dissertation, novel single- and multimicrophone speech dereverberation algorithms are developed that aim at the suppression of late reverberation, ie, at estimation of the early speech component This is done via so-called spectral enhancement techniques that require a specific measure of the late reverberant signal This measure, called spectral variance, can be estimated directly from the received (possibly noisy) reverberant signal(s) using a statistical reverberation model and a limited amount of a priori knowledge about the acoustic channel(s) between the source and the microphone(s) In our work an existing single-channel statistical reverberation model serves as a starting point The model is characterized by one parameter that depends on the acoustic characteristics of the environment We show that the spectral variance estimator that is based on this model, can only be used when the source-microphone distance is larger than the so-called critical distance This is, crudely speaking, the distance where the direct sound power is equal to the total reflective power A generalization of the statistical reverberation model in which the direct sound is incorporated is developed This model requires one additional parameter that is related to the ratio between the direct sound energy and the sound energy of all reflections The generalized model is used to derive a novel spectral variance estimator When the novel estimator is used for dereverberation rather than the existing estimator, and the source-microphone distance is smaller than the critical distance, the dereverberation performance is significantly increased Single-microphone systems only exploit the temporal and spectral diversity of the received signal Reverberation, of course, also induces spatial diversity To additionally exploit this diversity, multiple microphones must be used, and their outputs must be combined by a suitable spatial processor such as the so-called delay and sum beamformer It is not a priori evident whether spectral enhancement is best done before or after the spatial processor For this reason we investigate both possibilities, as well as a merge of the spatial processor and the spectral enhancement technique An advantage of the latter option is that the spectral variance estimator can be further improved Our experiments show that the use of multiple microphones affords a significant improvement of the perceptual speech quality The applicability of the theory developed in this dissertation is demonstrated using a hands-free communication system Since hands-free systems are often used in a noisy and reverberant environment, the received microphone signal does not only contain the desired signal but also interferences such as room reverberation that is caused by the desired source, background noise, and a far-end echo signal that results from a sound that is produced by the loudspeaker Usually an acoustic echo canceller is used to cancel the far-end echo Additionally a post-processor is used to suppress background noise and residual echo, ie, echo which could not be cancelled by the echo canceller In this work a novel structure and post-processor for an acoustic echo canceller are developed The post-processor suppresses late reverberation caused by the desired source, residual echo, and background noise The late reverberation and late residual echo are estimated using the generalized statistical reverberation model Experimental results convincingly demonstrate the benefits of the proposed system for suppressing late reverberation, residual echo and background noise The proposed structure and post-processor have a low computational complexity, a highly modular structure, can be seamlessly integrated into existing hands-free communication systems, and affords a significant increase of the listening comfort and speech intelligibility

/pdf/single-and-multi-microphone-speech-dereverberation-using-81pl8h93qv.pdf

Single- and multi-microphone speech dereverberation using spectral enhancement

Techniques to suppress noise from a signal comprised of speech plus noise. In accordance with aspects of the invention, two or more signal detectors (e.g., microphones) are used to detect respective signals having speech and noise components, with the magnitude of each component being dependent on various factors such as the distance between the speech source and the microphone. Signal processing is then used to process the detected signals to generate the desired output signal having predominantly speech with a large portion of the noise removed. The techniques described herein may be advantageously used for both near-field and far-field applications, and may be implemented in various mobile communication devices such as cellular phones.

Noise suppression for a wireless communication device

The acoustic theory for multichannel sound reproduction systems usually assumes free-field conditions for the listening environment. However, their performance in real-world listening environments may be impaired by reflections at the walls. This impairment can be reduced by suitable compensation measures. For systems with many channels, active compensation is an option, since the compensating waves can be created by the reproduction loudspeakers. Due to the time-varying nature of room acoustics, the compensation signals have to be determined by an adaptive system. The problems associated with the successful operation of multichannel adaptive systems are addressed in this contribution. First, a method for decoupling the adaptation problem is introduced. It is based on a generalized singular value decomposition and is called eigenspace adaptive filtering. Unfortunately, it cannot be implemented in its pure form, since the continuous adaptation of the generalized singular value decomposition matrices to the variable room acoustics is numerically very demanding. However, a combination of this mathematical technique with the physical description of wave propagation yields a realizable multichannel adaptation method with good decoupling properties. It is called wave domain adaptive filtering and is discussed here in the context of wave field synthesis.

Active listening room compensation for massive multichannel sound reproduction systems using wave-domain adaptive filtering.

To shorten an output delay while a high sound source separation performance is ensured when a sound separation process based on an ICA method is performed. A second Fourier transform process execution cycle t2 for obtaining a second frequency-domain signal S1 used as an input signal of a filter process is set shorter than a first Fourier transform process execution cycle t1 for obtaining a first frequency-domain signal used for a learning computation of a separating matrix. When the time length of a second time-domain signal S1 is set shorter than a time length of a first time-domain signal S0, a second separating matrix used for a filter process is set by aggregating matrix components of a first separating matrix obtained through a learning calculation for every a plurality of groups.

Sound source separation apparatus and sound source separation method

IWAENC2003: International Workshop on Acoustic Echo and Noise Control, September 8-11, 2003, Kyoto, Japan.

http://www.kecl.ntt.co.jp/icl/signal/iwaenc03/cdrom/data/0032.pdf

High-Fidelity Blind Separation of Acoustic Signals using SIMO-Model-based ICA with Information-Geometric Learning

This paper describes an improved complementary beamforming microphone array with a new noise adaptation. Complementary beamforming is based on two types of beamformers designed to obtain complementary directivity patterns. In this system, two directivity patterns of the beamformers are adapted to the noise directions so that the expectation values of each noise power spectrum are minimized. Using this technique, we can realize the directional nulls for each noise even when the number of sound sources exceeds that of microphones. To evaluate the effectiveness, speech enhancement experiments are performed based on computer simulations with a two-element array and three sound sources. Compared with the conventional spectral subtraction method cascaded with the adaptive beamformer, it is shown that the proposed array improves the signal-to-noise ratio of degraded speech by more than 6 dB and performs more than 18% better in word recognition rates when the interfering noise is two speakers.

Speech enhancement using nonlinear microphone array with noise adaptive complementary beamforming

This paper describes an improved spectral subtraction method by using the complementary beamforming microphone array to enhance noisy speech signals for speech recognition. The complementary beamforming is based on two types of beamformers designed to obtain complementary directivity patterns with respect to each other. It is shown that the nonlinear subtraction processing with complementary beamforming can result in a kind of the spectral subtraction without the need for speech pause detection. In addition, the design of the optimization algorithm for the directivity pattern is also described. To evaluate the effectiveness, speech enhancement experiments and speech recognition experiments are performed based on computer simulations. In comparison with the optimized conventional delay-and-sum array, it is shown that the proposed array improves the signal-to-noise ratio of degraded speech by about 2 dB and performs about 10% better in word recognition rates under heavy noisy conditions.

Speech enhancement using nonlinear microphone array with complementary beamforming

This paper proposes an efficient compensation method using a first-order approximation of time axis scaling for the variations of the room acoustic transfer function. The time axis scaling model is based on the fact that the change of the sound velocity due to the change of room temperature is a dominant factor for the variations of room impulse response affected by environmental conditions. In this paper, the effectiveness of the compensation method is evaluated using room impulse responses measured in the real environment. As the results, it is clarified that the variations of room impulse response can be modeled by the first-order approximated time axis scaling when the successive re-estimation is performed every small change of temperature. Furthermore, it is shown that the compensation method applied to an inverse filtering based dereverberation approach improves the intelligibility and speech recognition rates dramatically.

Compensating of room acoustic transfer functions affected by change of room temperature

SUMMARY In this paper, single-input multiple-output (SIMO)-modelbased blind source separation (BSS) is addressed, where unknown mixed source signals are detected at microphones, and can be separated, not into monaural source signals but into SIMO-model-based signals from independent sources as they are at the microphones. This technique is highly applicable to high-fidelity signal processing such as binaural signal processing. First, we provide an experimental comparison between two kinds of SIMOmodel-based BSS methods, namely, conventional frequency-domain ICA with projection-back processing (FDICA-PB), and SIMO-ICA which was recently proposed by the authors. Secondly, we propose a new combination technique of the FDICA-PB and SIMO-ICA, which can achieve a higher separation performance than the two methods. The experimental results reveal that the accuracy of the separated SIMO signals in the simple SIMO-ICA is inferior to that of the signals obtained by FDICA-PB under low-quality initial value conditions, but the proposed combination

PAPER Special Section on Adaptive Signal Processing and Its Applications Multistage SIMO-Model-Based Blind Source Separation Combining Frequency-Domain ICA and Time-Domain ICA

Hiroshi Saruwatari

Papers

High-Fidelity Blind Separation of Acoustic Signals using SIMO-Model-based ICA with Information-Geometric Learning

Speech enhancement using nonlinear microphone array with noise adaptive complementary beamforming

Speech enhancement using nonlinear microphone array with complementary beamforming

Compensating of room acoustic transfer functions affected by change of room temperature

PAPER Special Section on Adaptive Signal Processing and Its Applications Multistage SIMO-Model-Based Blind Source Separation Combining Frequency-Domain ICA and Time-Domain ICA