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In this paper, we provide a new theoretical analysis of the amount of musical noise generated via generalized spectral subtraction based on higher order statistics. Power spectral subtraction is the most commonly used spectral subtraction method, and in our previous study a musical noise assessment theory limited to the power spectral domain was proposed. In this paper, we propose a generalization...
Music is an art form in which sounds are organized in time; however, current approaches for determining similarity and classification largely ignore temporal information. This paper presents an approach to automatic tagging which incorporates temporal aspects of music directly into the statistical models, unlike the typical bag-of-frames paradigm in traditional music information retrieval techniques...
We propose a music segment detection method for audio signals. Unlike many existing methods, ours specifically focuses on a background-music detection task, that is, detecting music used in background of main sounds. This task is important because music is almost always overlapped by speech or other environmental sounds in visual materials such as TV programs. Our method consists of feature extraction,...
Given that most of the speech signal recordings are generally mixed with other sounds like music, songs, or noises and knowing that the processing of any speech signal will be easier when we separate the speech area from the non-speech area, we propose a preprocessing method for speech/ non speech discrimination which is also able to identify some acoustic sounds, by using some statistical observations...
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