Music emotion recognition by Yi-Hsuan Yang; Homer H Chen

By Yi-Hsuan Yang; Homer H Chen

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Some songs are recorded with a higher volume, while others are recorded with a lower volume). A possible approach of volume normalization is to look for the loudest volume of the audio waveform and then amplify or attenuate the entire waveform until the loudest volume reaches a particular value. ∗ Another approach is to normalize with respect to the root-mean-square (rms) energy or the sound pressure level (SPL) of music signal. 2) where p ref is the referenced sound pressure [43]. This approach is implemented in, for example, the MIR toolbox [182].

The toolbox is publicly available and very useful for music emotion research. 3, it is also important to model the time-varying relationship between music and emotion. Attempts have been made to automatically detect the emotion variation within a music piece, or music emotion variation detection (MEVD). Two approaches can be found in the literature. The first one exploits the temporal information of the music segments while computing the VA values, such as the time series analysis method [282] and the system identification method [173].

This example illustrates the importance of rhythm features. 3 Temporal Features We use the sound description toolbox [31] to extract zero-crossing rate, temporal centroid, and log attack time to capture the temporal quality of music. , sign changes). 6 Zero-crossing rate [31] of the four example songs. (Data form E. , Proc. Int. Conf. Music Information retrieval, 2007). where T is the length of the time window, s t is the magnitude of the t-th timedomain sample, and w (·) is a rectangular window.

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