NDLI: Genre based emotion annotation for music in noisy environment

Content Provider	IEEE Xplore Digital Library
Author	Yu-Hao Chin Po-Chuan Lin Tzu-Chiang Tai Jia-Ching Wang
Copyright Year	2015
Description	Author affiliation: Dept. of Comput. Sci. & Inf. Eng., Nat. Central Univ., Taoyuan, Taiwan (Yu-Hao Chin) \|\| Dept. of Electron. Eng. & Comput. Sci., Tungfang Design Univ., Taiwan (Po-Chuan Lin) \|\| Dept. of Comput. Sci. & Inf. Eng., Providence Univ., Taichung, Taiwan (Tzu-Chiang Tai; Jia-Ching Wang)
Abstract	The music listened by human is sometimes exposed to noise. For example, background noise usually exists when listening to music in broadcasts or lives. The noise will worsen the performance in various music emotion recognition systems. To solve the problem, this work constructs a robust system for music emotion classification in a noisy environment. Furthermore, the genre is considered when determining the emotional label for the song. The proposed system consists of three major parts, i.e. subspace based noise suppression, genre index computation, and support vector machine (SVM). Firstly, the system uses noise suppression to remove the noise content in the signal. After that, acoustical features are extracted from each music clip. Next, a dictionary is constructed by using songs that cover a wide range of genres, and it is adopted to implement sparse coding. Via sparse coding, data can be transformed to sparse coefficient vectors, and this paper computes genre indexes for the music genres based on the sparse coefficient vector. The genre indexes are regarded as combination weights in the latter phase. At the training stage of the SVM, this paper train emotional models for each genre. At the prediction stage, the predictions that obtained by emotional models in each genre are weighted combined across all genres using the genre indexes. Finally, the proposed system annotates multiple emotional labels for a song based on the combined prediction. The experimental result shows that the system can achieve a good performance in both normal and noisy environments.
Starting Page	863
Ending Page	866
File Size	960226
Page Count	4
File Format	PDF
ISSN	21568111
e-ISBN	9781479999538
DOI	10.1109/ACII.2015.7344675
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Emotion recognition Noisy environment Music Emotion and genre Music emotion classification Feature extraction Sparse representation Robustness Hierarchical system Indexes Noise measurement
Content Type	Text
Resource Type	Article

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