Anechoic Data Research Articles

Classification of reverberant audio signals using clustered ad hoc distributed microphones

In a real world scenario, the automatic classification of audio signals constitutes a difficult problem. Often, reverberation and interfering sounds reduce the quality of a target source signal. This results in a mismatch between test and training data when a classifier is trained on clean and anechoic data. To classify disturbed signals more accurately we make use of the spatial distribution of microphones from ad hoc microphone arrays. In the proposed algorithm clusters of microphones that either are dominated by one of the sources in an acoustic scenario or contain mainly signal mixtures and reverberation are estimated in the audio feature domain. Information is shared within and in between these clusters to create one feature vector for each cluster to classify the source dominating this cluster. We evaluate the algorithm using simultaneously active sound sources and different ad hoc microphone arrays in simulated reverberant scenarios and multichannel recordings of an ad hoc microphone setup in a real environment. The cluster based classification accuracy is higher than the accuracy based on single microphone signals and allows for a robust classification of simultaneously active sources in reverberant environments.

Amplitude modulation detection by human listeners in sound fields.

The temporal modulation transfer function (TMTF) approach allows techniques from linear system analysis to be used to predict how the auditory system will respond to arbitrary patterns of amplitude modulation (AM). Although this approach forms the basis for a standard method of predicting speech intelligibility based on estimates of the acoustical modulation transfer function (MTF) between source and receiver, human sensitivity to AM as characterized by the TMTF has not been extensively studied under realistic listening conditions, such as in reverberant sound fields. Here, TMTFs (octave bands from 2–512 Hz) were obtained in three listening conditions simulated using virtual auditory space techniques: diotic, anechoic sound field, and reverberant room sound field. TMTFs were then related to acoustic MTFs estimated using two different methods in each of the listening conditions. Both diotic and anechoic data were found to be in good agreement with classic results, but AM thresholds in the reverberant room were lower than predictions based on acoustic MTFs. This result suggests that simple linear systems techniques may not be appropriate for predicting TMTFs from acoustical MTFs in reverberant sound fields, and may be suggestive of mechanisms that functionally enhance modulation during reverberant listening.