Auditory scene analysis based on time‐frequency integration of shared FM and AM (I): Lagrange differential features and frequency‐axis integration

Abstract

SUMMARYWe will propose in this paper a new algorithm for a computational implementation of auditory scene analysis. This algorithm forms a three-layer structure of (1) subband decomposition by wavelet transform, (2) characterization of subband signal fragments by instantaneous frequency, frequency change rate, and amplitude change rate, and (3) frequency integration of subband signal features by voting method. We will perform the grouping and integration by voting the subband signal fragments into a nonparametric multipeak probability density distribution expressing "possibility of streams"; and then the recognition of the streams and the extraction of the stream parameters are realized by tracing its greatest point. It is confirmed from basic experiments for synthesized sounds and voices that the fundamental frequency/frequency change rate/amplitude change rate can be separated and estimated from multiple streams.

Auditory scene analysis based on time‐frequency integration of shared FM and AM (II): Optimum time‐domain integration and stream sound reconstruction

“…Here, we will briefly summarize the frequency-axis integration method of shared FM and AM proposed in the preceding paper [1]. The outputs up to this stage give the observation probability density distribution for the nonparametric Kalman filter ( Fig.…”

“…For confirming the fundamental performance of the proposed technique, the experiment is performed for the computer synthesized sound first. It is composed of the 7-component sound of falling frequency (stream 1) and the 4-component sound of rising frequency (stream 2); and is the same one used in the evaluation of the preceding paper [1]. The parameters of the synthesized sound are shown in Table 2 and the waveform and the instantaneous amplitude of the subband are shown in Fig.…”

“…To evaluate the effect of the nonparametric Kalman filter, Table 3 shows the comparison of the average errors with respect to the theoretical values with the technique (traces of the peaks of the voted distributions) of the pre- ceding paper [1]. The fundamental frequency is determined with a very high accuracy and there is almost no difference in the two cases; however, for the frequency change rate and amplitude change rate, the accuracy is raised by the proposed technique.…”

“…We have started out from the problem of the computational auditory scene analysis for an intelligent acoustic sensor [7], and proposed a method for auditory scene analysis based on time-frequency integration of shared FM and AM. In the preceding paper [1], as the main point of the © 2002 Wiley Periodicals, Inc.…”