Fundamentals of speech recognition
Fundamentals of speech recognition
Speaker change detection and tracking in real-time news broadcasting analysis
Proceedings of the tenth ACM international conference on Multimedia
Music summarization using key phrases
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 02
Repeating pattern discovery and structure analysis from acoustic music data
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
Content-based music structure analysis with applications to music semantics understanding
Proceedings of the 12th annual ACM international conference on Multimedia
Automatic music video summarization based on audio-visual-text analysis and alignment
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Multimodal content-based structure analysis of karaoke music
Proceedings of the 13th annual ACM international conference on Multimedia
Automatic Structure Detection for Popular Music
IEEE MultiMedia
Automatic summarization of music videos
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Semantic context detection using audio event fusion: camera-ready version
EURASIP Journal on Applied Signal Processing
Scalable music recommendation by search
Proceedings of the 15th international conference on Multimedia
Music structure analysis using a probabilistic fitness measure and a greedy search algorithm
IEEE Transactions on Audio, Speech, and Language Processing
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Similar to image and video thumbnail, music snippet is defined as the most representative or highlight excerpt of a music clip, and can be used efficiently for fast browsing large number of music files. Music snippet is usually a part of the repeated melody, main theme or chorus. In this paper, we present an approach to extracting music snippet automatically. In our approach, the most salient segment of the music is firstly detected based on its occurrence frequency and energy information. Meanwhile, the boundaries of musical phrases are also detected based on the estimated phrase length and phrase boundary confidence of each frame. These boundaries are used to ensure that an extracted snippet does not break musical phrases. Finally, the musical phrases including the most salient segment are extracted as music snippet. User study indicates that the proposed algorithm works very well on our music database.