Temporal and Spectral Processing Methods for Processing of Degraded Speech: A Review <s> CASA Methods <s> Auditory scene analysis (ASA) is the process by which the auditory system separates the individual sounds in natural-world situations, in which these sounds are usually interleaved and overlapped in time and their components interleaved and overlapped in frequency. ASA is difficult because the ear has access only to the single pressure wave that is the sum of the pressure waves coming from all the individual sound sources (such as human voices, or foot steps). To construct a separate mental description for each source, ASA must analyze the incoming signal by the use of heuristic processes. These are based on regularities in the incoming sound that result from the fact that it is really the sum of some underlying sounds. For example, the incoming sound is built up of many frequency components. If a set, A, of these frequency components begin together at exactly the same time, whereas another set B all begin together, but at a different time from those of A, then the components of A will be considered as parts of one sound, and those of B as parts of a different sound. This grouping is justified by a regularity in the environment, namely that all the frequency components of a single sound tend to start at the same time. Other heuristic analyses are based on different regularities in how sound is produced. The grouping of these components can determine the perceived pitch, timbre, loudness, and spatial position of the resulting sounds. <s> BIB001 </s> Temporal and Spectral Processing Methods for Processing of Degraded Speech: A Review <s> CASA Methods <s> Segregating speech from one monaural recording has proven to be very challenging. Monaural segregation of voiced speech has been studied in previous systems that incorporate auditory scene analysis principles. A major problem for these systems is their inability to deal with the high-frequency part of speech. Psychoacoustic evidence suggests that different perceptual mechanisms are involved in handling resolved and unresolved harmonics. We propose a novel system for voiced speech segregation that segregates resolved and unresolved harmonics differently. For resolved harmonics, the system generates segments based on temporal continuity and cross-channel correlation, and groups them according to their periodicities. For unresolved harmonics, it generates segments based on common amplitude modulation (AM) in addition to temporal continuity and groups them according to AM rates. Underlying the segregation process is a pitch contour that is first estimated from speech segregated according to dominant pitch and then adjusted according to psychoacoustic constraints. Our system is systematically evaluated and compared with pervious systems, and it yields substantially better performance, especially for the high-frequency part of speech. <s> BIB002 </s> Temporal and Spectral Processing Methods for Processing of Degraded Speech: A Review <s> CASA Methods <s> A human listener has the remarkable ability to segregate an acoustic mixture and attend to a target sound. This perceptual process is called auditory scene analysis (ASA). Moreover, the listener can accomplish much of auditory scene analysis with only one ear. Research in ASA has inspired many studies in computational auditory scene analysis (CASA) for sound segregation. In this chapter we introduce a CASA approach to monaural speech segregation. After a brief overview of CASA, we present in detail a CASA system that segregates both voiced and unvoiced speech. Our description covers the major stages of CASA, including feature extraction, auditory segmentation, and grouping. <s> BIB003 </s> Temporal and Spectral Processing Methods for Processing of Degraded Speech: A Review <s> CASA Methods <s> We present a new technique for separating two speech signals from a single recording. The proposed method bridges the gap between underdetermined blind source separation techniques and those techniques that model the human auditory system, that is, computational auditory scene analysis (CASA). For this purpose, we decompose the speech signal into the excitation signal and the vocal-tract-related filter and then estimate the components from the mixed speech using a hybrid model. We first express the probability density function (PDF) of the mixed speech's log spectral vectors in terms of the PDFs of the underlying speech signal's vocal-tract-related filters. Then, the mean vectors of PDFs of the vocal-tract-related filters are obtained using a maximum likelihood estimator given the mixed signal. Finally, the estimated vocal-tract-related filters along with the extracted fundamental frequencies are used to reconstruct estimates of the individual speech signals. The proposed technique effectively adds vocal-tract-related filter characteristics as a new cue to CASA models using a new grouping technique based on an underdetermined blind source separation. We compare our model with both an underdetermined blind source separation and a CASA method. The experimental results show that our model outperforms both techniques in terms of SNR improvement and the percentage of crosstalk suppression. <s> BIB004
While speech enhancement using signal processing methods with satisfactory performance remains a challenge, the natural ability to enhance sounds of interest selectively by the human auditory system inspired researchers to approach this issue in a different way. In 1990, Bregman proposed the concept of auditory scene analysis (ASA) to segregate acoustic signal into streams, which correspond to different sources BIB001 . A typical ASA system generally consists of two main stages: Segmentation (analysis) and grouping (synthesis). In the first stage, the mixture sound is segmented into the time-frequency cells. Segmentation is performed using either the STFT or the gammatone filter bank  . The segments are then grouped, based on the cues that are mainly onset and offset, on harmonicity, and on position cues BIB003 . This ASA account has inspired a series of computational ASA (CASA) systems for sound segregation  . A main advantage of CASA is that it does not make strong assumptions about interference. Generally, a typical CASA system contains four stages: peripheral analysis, feature extraction, segmentation, and grouping BIB003 . The peripheral processing decomposes the auditory scene into a time-frequency (T-F) representation via bandpass filtering and time windowing. The second stage extracts auditory features corresponding to ASA cues. In segmentation and grouping, the system generates segments for both target and interference, and groups the segments originating from the target into a target stream. Finally, the waveform of the segregated target is synthesized from the target stream BIB003 . The techniques based on CASA suffer from two problems. First, these techniques are not able to separate unvoiced segments and almost in all reported results, one or both underlying signals are fully voiced BIB002 . Second, the vocal-tract related filter characteristics are not included in the discriminative cues for separation. In other words, in CASA techniques, the role of the excitation signal is more important than the vocal tract shape BIB004 .