An Online Solution for Localisation, Tracking and Separation of Moving Speech Sources

The problem of separating a time varying number of speech sources in a room is difficult to solve. The challenge lies in estimating the number and the location of these speech sources. Furthermore, the tracked speech sources need to be separated. This thesis proposes a solution which utilises the Random Finite Set approach to estimate the number and location of these speech sources and subsequently separate the speech source mixture via time frequency masking

Acoustic Speaker Localization with Strong Reverberation and Adaptive Feature Filtering with a Bayes RFS Framework

The thesis investigates the challenges of speaker localization in presence of strong reverberation, multi-speaker tracking, and multi-feature multi-speaker state filtering, using sound recordings from microphones. Novel reverberation-robust speaker localization algorithms are derived from the signal and room acoustics models. A multi-speaker tracking filter and a multi-feature multi-speaker state filter are developed based upon the generalized labeled multi-Bernoulli random finite set framework. Experiments and comparative studies have verified and demonstrated the benefits of the proposed methods

Video-aided model-based source separation in real reverberant rooms

Source separation algorithms that utilize only audio data can perform poorly if multiple sources or reverberation are present. In this paper we therefore propose a video-aided model-based source separation algorithm for a two-channel reverberant recording in which the sources are assumed static. By exploiting cues from video, we first localize individual speech sources in the enclosure and then estimate their directions. The interaural spatial cues, the interaural phase difference and the interaural level difference, as well as the mixing vectors are probabilistically modeled. The models make use of the source direction information and are evaluated at discrete timefrequency points. The model parameters are refined with the wellknown expectation-maximization (EM) algorithm. The algorithm outputs time-frequency masks that are used to reconstruct the individual sources. Simulation results show that by utilizing the visual modality the proposed algorithm can produce better timefrequency masks thereby giving improved source estimates. We provide experimental results to test the proposed algorithm in different scenarios and provide comparisons with both other audio-only and audio-visual algorithms and achieve improved performance both on synthetic and real data. We also include dereverberation based pre-processing in our algorithm in order to suppress the late reverberant components from the observed stereo mixture and further enhance the overall output of the algorithm. This advantage makes our algorithm a suitable candidate for use in under-determined highly reverberant settings where the performance of other audio-only and audio-visual methods is limited

Studies on noise robust automatic speech recognition

Noise in everyday acoustic environments such as cars, traffic environments, and cafeterias remains one of the main challenges in automatic speech recognition (ASR). As a research theme, it has received wide attention in conferences and scientific journals focused on speech technology. This article collection reviews both the classic and novel approaches suggested for noise robust ASR. The articles are literature reviews written for the spring 2009 seminar course on noise robust automatic speech recognition (course code T-61.6060) held at TKK

Informed algorithms for sound source separation in enclosed reverberant environments

While humans can separate a sound of interest amidst a cacophony of contending sounds in an echoic environment, machine-based methods lag behind in solving this task. This thesis thus aims at improving performance of audio separation algorithms when they are informed i.e. have access to source location information. These locations are assumed to be known a priori in this work, for example by video processing. Initially, a multi-microphone array based method combined with binary time-frequency masking is proposed. A robust least squares frequency invariant data independent beamformer designed with the location information is utilized to estimate the sources. To further enhance the estimated sources, binary time-frequency masking based post-processing is used but cepstral domain smoothing is required to mitigate musical noise. To tackle the under-determined case and further improve separation performance at higher reverberation times, a two-microphone based method which is inspired by human auditory processing and generates soft time-frequency masks is described. In this approach interaural level difference, interaural phase difference and mixing vectors are probabilistically modeled in the time-frequency domain and the model parameters are learned through the expectation-maximization (EM) algorithm. A direction vector is estimated for each source, using the location information, which is used as the mean parameter of the mixing vector model. Soft time-frequency masks are used to reconstruct the sources. A spatial covariance model is then integrated into the probabilistic model framework that encodes the spatial characteristics of the enclosure and further improves the separation performance in challenging scenarios i.e. when sources are in close proximity and when the level of reverberation is high. Finally, new dereverberation based pre-processing is proposed based on the cascade of three dereverberation stages where each enhances the twomicrophone reverberant mixture. The dereverberation stages are based on amplitude spectral subtraction, where the late reverberation is estimated and suppressed. The combination of such dereverberation based pre-processing and use of soft mask separation yields the best separation performance. All methods are evaluated with real and synthetic mixtures formed for example from speech signals from the TIMIT database and measured room impulse responses