Computational Auditory Scene Analysis Systems Research Articles

PITCH ESTIMATION FRAMEWORK FOR SPEECH SEGREGATION USING COCHLEAGRAM MORPHING

Computational auditory scene analysis (CASA) has significant role in speech segregation from monaural audio mixtures and generally a measure for performance of speech recognition systems. Pitch estimation has a substantial role in performance of CASA systems. This study presents a novel pitch estimation framework for speech segregation from monaural audio mixtures using cochleagram morphing. The proposed framework takes the rough estimation of target pitch from given audio mixtures containing speech and background interferences. Discrete set consisting morphed versions of cochleagram is obtained using k-Means clustering. The estimated pitch values are improved by validating and smoothing them to morphed cochleagram. Measure of refined estimated pitch contours along with harmonicity and temporal continuity are used to segregate target speech. The proposed framework produced 83.13% accuracy for MIR-1k dataset which is considerably higher than the existing methods.

A Bayesian computational basis for auditory selective attention using head rotation and the interaural time-difference cue.

The process of resolving mixtures of several sounds into their separate individual streams is known as auditory scene analysis and it remains a challenging task for computational systems. It is well-known that animals use binaural differences in arrival time and intensity at the two ears to find the arrival angle of sounds in the azimuthal plane, and this localization function has sometimes been considered sufficient to enable the un-mixing of complex scenes. However, the ability of such systems to resolve distinct sound sources in both space and frequency remains limited. The neural computations for detecting interaural time difference (ITD) have been well studied and have served as the inspiration for computational auditory scene analysis systems, however a crucial limitation of ITD models is that they produce ambiguous or “phantom” images in the scene. This has been thought to limit their usefulness at frequencies above about 1khz in humans. We present a simple Bayesian model and an implementation on a robot that uses ITD information recursively. The model makes use of head rotations to show that ITD information is sufficient to unambiguously resolve sound sources in both space and frequency. Contrary to commonly held assumptions about sound localization, we show that the ITD cue used with high-frequency sound can provide accurate and unambiguous localization and resolution of competing sounds. Our findings suggest that an “active hearing” approach could be useful in robotic systems that operate in natural, noisy settings. We also suggest that neurophysiological models of sound localization in animals could benefit from revision to include the influence of top-down memory and sensorimotor integration across head rotations.

A Tandem Algorithm for Pitch Estimation and Voiced Speech Segregation

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> A lot of effort has been made in computational auditory scene analysis (CASA) to segregate speech from monaural mixtures. The performance of current CASA systems on voiced speech segregation is limited by lacking a robust algorithm for pitch estimation. We propose a tandem algorithm that performs pitch estimation of a target utterance and segregation of voiced portions of target speech jointly and iteratively. This algorithm first obtains a rough estimate of target pitch, and then uses this estimate to segregate target speech using harmonicity and temporal continuity. It then improves both pitch estimation and voiced speech segregation iteratively. Novel methods are proposed for performing segregation with a given pitch estimate and pitch determination with given segregation. Systematic evaluation shows that the tandem algorithm extracts a majority of target speech without including much interference, and it performs substantially better than previous systems for either pitch extraction or voiced speech segregation. </para>

A Tandem Algorithm for Pitch Estimation and Voiced Speech Segregation

A lot of effort has been made in computational auditory scene analysis (CASA) to segregate speech from monaural mixtures. The performance of current CASA systems on voiced speech segregation is lim...

Computational Auditory Scene Analysis: Principles, Algorithms and Applications

First Page

Computational auditory scene analysis-constrained array processing for sound source separation

In this work, techniques are developed and studied for the extraction of single-source acoustic signals out of multi-source signals. Such extracted signals can be used in a variety of applications including: automatic speech recognition, teleconferencing, and robot auditory systems. Most previous approaches fall into two categories: computational auditory scene analysis (CASA) and array signal processing. The approach taken here is to combine these complementary techniques into an integrated one: CASA-constrained array processing. In principle, this integrated approach should provide a performance gain since the information used by array processing (direction of propagation through a sound-field) is independent of other CASA features (fundamental frequency, on/offset, etc.). One difficulty encountered by CASA that can be overcome by array processing is the sequential grouping of spectrally dissimilar phonemes in a speech signal, such as a fricative followed by a vowel. The method presented here differs from standard array processing by the addition of CASA features for the signal separation decision. Compared to other CASA systems that use binaural cues, it: (1) is not limited to two microphones (since the goal is not auditory system modeling); and (2) makes complete use of source location and other CASA features—for simultaneous and sequential grouping.