Michael Kleinschmidt

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Psychoacoustical and neurophysiological results indicate that spectro-temporal modulations play an important role in sound perception. Speech signals, in particular, exhibit distinct spectro-temporal patterns which are well matched by receptive fields of cortical neurons. In order to improve the performance of automatic speech recognition (ASR) systems a(More)
A main task for computational auditory scene analysis (CASA) is to separate several concurrent speech sources. From psychoacoustics it is known that common onsets, common amplitude modulation and sound source direction are among the important cues which allow the separation for the human auditory system. A new algorithm is presented here, that performs(More)
In this paper a new approach is presented for estimating the long-term speechto-noise ratio (SNR) in individual frequency bands that is based on methods known from automatic speech recognition (ASR). It uses a model of auditory perception as front end, physiologically and psychoacoustically motivated sigma-pi cells as secondary features, and a linear or(More)
A novel noise suppression scheme for speech signals is proposed which is based on a neurophysiologically-motivated estimation of the local signal-to-noise ratio (SNR) in different frequency channels. For SNR-estimation, the input signal is transformed into so-called Amplitude Modulation Spectrograms (AMS), which represent both spectral and temporal(More)
In the European norm DIN EN ISO 3382 [1] about the “measurement of reverberation time of rooms with hints to other acoustical parameters” an early-tolate energy ratio is defined as a parameter that represents a ratio of early reflections energy to energies of reflections arriving after a certain critical delay time. The background understanding consists of(More)