AE-3.1

Adaptive Feedback Cancelling in Subbands for Hearing Aids
Sigisbert Wyrsch, August Kaelin (Swiss Federal Institute of Technology (ETH))

In this paper a hearing aid concept with recruitment of loudness compensation and acoustic feedback cancellation is presented. Special consideration is given to the acoustic feedback canceler which uses only the available (e.g. speech) input signal for adaptation. In principle, the feedback canceler is adapted to the feedback path in the transform domain using a power-normalized least mean square (LMS) algorithm. The transformation into uniform subbands is based on an augmentation of the modulated lapped transform (MLT). Together with the hearing-loss compensating forward filter the proposed feedback canceler is computationally very efficient.

AE-3.2

Bias Analysis in Continuous Adaptation Systems for Hearing Aids
Marcio G Siqueira, Abeer Alwan (University of California, Los Angeles)

This paper studies analytically the steady-state convergence behavior of adaptive algorithms that approximate the Wiener solution when operating in continuous adaptation to reduce acoustic feedback in hearing aids. A bias is found in the adaptive filter's estimate of the hearing-aid feedback path when the input signal is not white. Delays in the forward and cancellation paths are shown to reduce the magnitude of the bias. Equations for the bias transfer function are obtained. A discussion about properties of the bias when delays are placed in the forward and cancellation paths follows.

AE-3.3

MULTI-PITCH AND PERIODICITY ANALYSIS MODEL FOR SOUND SEPARATION AND AUDITORY SCENE ANALYSIS
Matti Karjalainen, Tero Tolonen (Helsinki University of Technology, Laboratory of Acoustics and Audio Signal Processing)

A model for multi-pitch and periodicity analysis of complex audio signals is presented that is more efficient and practical than the Meddis and O'Mard unitary pitch perception model, yet exhibits very similar behavior. In this paper we also demonstrate how to apply this model to source separation of complex audio signals such as polyphonic and multi-instrumental music and mixtures of simultaneous speakers. Such analysis techniques are important for automatic transcription of music and structural representation of audio signals. (See also: http://www.acoustics.hut.fi/~ttolonen/icassp99/pitchdet/)

AE-3.4

A COMPARISON USING SIGNAL DETECTION THEORY OF THE ABILITY OF TWO COMPUTATIONAL AUDITORY MODELS TO PREDICT EXPERIMENTAL DATA
Lisa C Gresham, Leslie M Collins (Department of Electrical and Computer Engineering, Duke University)

In order to develop improved remediation techniques for hearing impairment, auditory researchers must gain a greater understanding of the relation between the psychophysics of hearing and the underlying physiology. One approach to studying the auditory system has been to design computational auditory models that predict neurophysiological data such as neural firing rates (Patterson et al., 1995; Carney, 1993). To link these physiologically-based models to psychophysics, theoretical bounds on detection performance have been derived using signal detection theory to analyze the simulated data for various psychophysical tasks (Siebert, 1968). Previous efforts, including our own recent work using the Auditory Image Model, have demonstrated the validity of this type of analysis; however, theoretical predictions often exceed experimentally-measured performance (Gresham and Collins, 1998; Siebert, 1970). In this paper, we compare predictions of detection performance across several computational auditory models. We reconcile some of the previously observed discrepancies by incorporating phase uncertainty into the optimal detector.

AE-3.5

Adaptive Eigenvalue Decomposition Algorithm For Realtime Acoustic Source Localization System
Yiteng Huang (Georgia Institute of Technology), Jacob Benesty, Gary W Elko (Bell Labs)

To locate an acoustic source in a room, the relative delay between microphone pairs must be determined efficiently and accurately. However, most traditional time delay estimation (TDE) algorithms fail in reverberant environments. In this paper, a new approach is proposed that takes into account the reverberation of the room. A realtime PC-based TDE system running under Microsoft Windows system was developed with three TDE techniques: classical cross-correlation, phase transform, and a new algorithm that is proposed in this paper. The system provides an interactive platform that allows users to compare performance of these algorithms.

AE-3.6

Compensating of Room Acoustic Transfer Functions Affected by Change of Room Temperature
Michiaki Omura (Switching Division, NEC Corporation), Motohiko Yada, Hiroshi Saruwatari (Graduate School of Engineering, Nagoya University), Shoji Kajita (Center for Information Media Studies, Nagoya University), Kazuya Takeda (Graduate School of Engineering, Nagoya University), Fumitada Itakura (Center for Information Media Studies, Nagoya University)

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.

AE-3.7

`Perfect Reconstruction' Time-Scaling Filterbanks
Thomas F Quatieri (MIT Lincoln Laboratory), Thomas E Hanna (Naval Submarine Medical Research Laboratory)

A filterbank-based method of time-scale modification is analyzed for elemental signals including clicks, sines, and AM-FM sines. It is shown that with the use of some basic properties of linear systems, as well as FM-to-AM filter transduction, "perfect reconstruction" time-scaling filterbanks can be constructed for these elemental signal classes under certain conditions on the filterbank. Conditions for perfect reconstruction time-scaling are shown analytically for the uniform filterbank case, while empirically for the nonuniform constant-Q (gammatone) case. Extension of perfect reconstruction to multi-components signals is shown to require both filterbank and signal-dependent conditions and indicates the need for a more complete theory of "perfect reconstruction" time-scaling filterbanks.

AE-3.8

An Adaptive Microphone Array with Good Sound Quality Using Auxiliary Fixed Beamformers and Its DSP Implementation
Osamu Hoshuyama, Akihiko Sugiyama (NEC Corporation)

This paper presents an adaptive microphone array using two auxiliary fixed beamfomers for good sound quality. One auxiliary fixed beamfomer is introduced in the target signal path to avoid suppression of high-frequency components in the total output. The other auxiliary fixed beamfomer is used for adaptation-mode control to eliminate the hysteresis in the relationship between signal direction and sensitivity. Both auxiliary fixed beamfomers bring about good sound quality, which improve intelligibility in speech communications and speech recognition rate. The proposed microphone array is implemented on a DSP system, which demonstrates flat frequency response and less hysteresis in its directivity pattern.

AE-3.9

An Event-Based Method For Microphone Array Speech Enhancement
Michael S Brandstein (Division of Engineering and Applied Sciences, Harvard University)

This paper presents the Multi-Channel Multi-Pulse (MCMP) algorithm for the enhancement of speech degraded by reverberations and additive noise. The enhanced speech is synthesized from a sequence of impulses exciting a linear predictive filter. The excitation signal is computed from a nonlinear process which uses impulse clustering of the multi-channel speech data to discriminate portions of the linear prediction residual produced by the desired speech signal from those due to multipath effects and uncorrelated noise. The MCMP algorithm is shown to be capable of identifying and attenuating reverberant portions of the speech signal as well as reducing the effects of additive noise.

< AE-2 AE-4 >

Last Update: February 4, 1999 Ingo Höntsch