Blind Speech Separation
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Blind Speech Separation
Author: Shoji Makino
language: en
Publisher: Springer Science & Business Media
Release Date: 2007-09-07
This is the world’s first edited book on independent component analysis (ICA)-based blind source separation (BSS) of convolutive mixtures of speech. This book brings together a small number of leading researchers to provide tutorial-like and in-depth treatment on major ICA-based BSS topics, with the objective of becoming the definitive source for current, comprehensive, authoritative, and yet accessible treatment.
Audio Source Separation
This book provides the first comprehensive overview of the fascinating topic of audio source separation based on non-negative matrix factorization, deep neural networks, and sparse component analysis. The first section of the book covers single channel source separation based on non-negative matrix factorization (NMF). After an introduction to the technique, two further chapters describe separation of known sources using non-negative spectrogram factorization, and temporal NMF models. In section two, NMF methods are extended to multi-channel source separation. Section three introduces deep neural network (DNN) techniques, with chapters on multichannel and single channel separation, and a further chapter on DNN based mask estimation for monaural speech separation. In section four, sparse component analysis (SCA) is discussed, with chapters on source separation using audio directional statistics modelling, multi-microphone MMSE-based techniques and diffusion map methods. The book brings together leading researchers to provide tutorial-like and in-depth treatments on major audio source separation topics, with the objective of becoming the definitive source for a comprehensive, authoritative, and accessible treatment. This book is written for graduate students and researchers who are interested in audio source separation techniques based on NMF, DNN and SCA.
Speech Separation by Humans and Machines
Author: Pierre Divenyi
language: en
Publisher: Springer Science & Business Media
Release Date: 2006-01-16
There is a serious problem in the recognition of sounds. It derives from the fact that they do not usually occur in isolation but in an environment in which a number of sound sources (voices, traffic, footsteps, music on the radio, and so on) are active at the same time. When these sounds arrive at the ear of the listener, the complex pressure waves coming from the separate sources add together to produce a single, more complex pressure wave that is the sum of the individual waves. The problem is how to form separate mental descriptions of the component sounds, despite the fact that the “mixture wave” does not directly reveal the waves that have been summed to form it. The name auditory scene analysis (ASA) refers to the process whereby the auditory systems of humans and other animals are able to solve this mixture problem. The process is believed to be quite general, not specific to speech sounds or any other type of sounds, and to exist in many species other than humans. It seems to involve assigning spectral energy to distinct “auditory objects” and “streams” that serve as the mental representations of distinct sound sources in the environment and the patterns that they make as they change over time. How this energy is assigned will affect the perceived n- ber of auditory sources, their perceived timbres, loudnesses, positions in space, and pitches.