Visualizing music and audio using self-similarity
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 1)
Automated extraction of music snippets
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
General sound classification and similarity in MPEG-7
Organised Sound
Automatic Structure Detection for Popular Music
IEEE MultiMedia
Music structure analysis by finding repeated parts
Proceedings of the 1st ACM workshop on Audio and music computing multimedia
Similarity matrix processing for music structure analysis
Proceedings of the 1st ACM workshop on Audio and music computing multimedia
Using duration models to reduce fragmentation in audio segmentation
Machine Learning
Automated analysis of musical structure
Automated analysis of musical structure
Music summarization using key phrases
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 02
Multiple scale music segmentation using rhythm, timbre, and harmony
EURASIP Journal on Applied Signal Processing
Towards structural analysis of audio recordings in the presence of musical variations
EURASIP Journal on Applied Signal Processing
Automatic transcription of melody, bass line, and chords in polyphonic music
Computer Music Journal
Structural Segmentation of Musical Audio by Constrained Clustering
IEEE Transactions on Audio, Speech, and Language Processing
Analysis of the meter of acoustic musical signals
IEEE Transactions on Audio, Speech, and Language Processing
Audio thumbnailing of popular music using chroma-based representations
IEEE Transactions on Multimedia
Mining transposed motifs in music
Journal of Intelligent Information Systems
Music segmentation and summarization based on self-similarity matrix
Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication
Elastic Net subspace clustering applied to pop/rock music structure analysis
Pattern Recognition Letters
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This paper proposes a method for recovering the sectional form of a musical piece from an acoustic signal. The description of form consists of a segmentation of the piece into musical parts, grouping of the segments representing the same part, and assigning musically meaningful labels, such as "chorus" or "verse," to the groups. The method uses a fitness function for the descriptions to select the one with the highest match with the acoustic properties of the input piece. Different aspects of the input signal are described with three acoustic features: mel-frequency cepstral coefficients, chroma, and rhythmogram. The features are used to estimate the probability that two segments in the description are repeats of each other, and the probabilities are used to determine the total fitness of the description. Creating the candidate descriptions is a combinatorial problem and a novel greedy algorithm constructing descriptions gradually is proposed to solve it. The group labeling utilizes a musicological model consisting of N-grams. The proposed method is evaluated on three data sets of musical pieces with manually annotated ground truth. The evaluations show that the proposed method is able to recover the structural description more accurately than the state-of-the-art reference method.