文件名称:2001_amta_aadwt
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- 2015-10-07
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The Discrete Wavelet Transform (DWT) is a transformation that can be used to analyze the
temporal and spectral properties of non-stationary signals like audio. In this paper we describe some
applications of the DWT to the problem of extracting information non-speech audio. More specifically
automatic classification of various types of audio using the DWT is described and compared with other
traditional feature extractors proposed in the literature. In addition, a technique for detecting the beat attributes
of music is presented. Both synthetic and real world stimuli were used to uate the performance of the beat
detection algorithm.-The Discrete Wavelet Transform (DWT) is a transformation that can be used to analyze the
temporal and spectral properties of non-stationary signals like audio. In this paper we describe some
applications of the DWT to the problem of extracting information non-speech audio. More specifically
automatic classification of various types of audio using the DWT is described and compared with other
traditional feature extractors proposed in the literature. In addition, a technique for detecting the beat attributes
of music is presented. Both synthetic and real world stimuli were used to uate the performance of the beat
detection algorithm.
temporal and spectral properties of non-stationary signals like audio. In this paper we describe some
applications of the DWT to the problem of extracting information non-speech audio. More specifically
automatic classification of various types of audio using the DWT is described and compared with other
traditional feature extractors proposed in the literature. In addition, a technique for detecting the beat attributes
of music is presented. Both synthetic and real world stimuli were used to uate the performance of the beat
detection algorithm.-The Discrete Wavelet Transform (DWT) is a transformation that can be used to analyze the
temporal and spectral properties of non-stationary signals like audio. In this paper we describe some
applications of the DWT to the problem of extracting information non-speech audio. More specifically
automatic classification of various types of audio using the DWT is described and compared with other
traditional feature extractors proposed in the literature. In addition, a technique for detecting the beat attributes
of music is presented. Both synthetic and real world stimuli were used to uate the performance of the beat
detection algorithm.
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2001_amta_aadwt.pdf