Unsupervised Detection of Music Boundaries by Time Series Structure Features
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Summary
This paper proposes an unsupervised method for boundary detection, combining three basic principles: novelty, homogeneity, and repetition, which is applicable to a wide range of time series beyond the music and audio domains.
- Type
- article
- Published
- 2012-07-22
- Cited by
- 80
- References
- 28
- Access
- Open access
- OpenAlex
- https://openalex.org/W78743328
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:14431478
Keywords
Computer science, Novelty detection, Novelty, Series (stratigraphy), Benchmark (surveying)
References
- Using Musical Structure to Enhance Automatic Chord Transcription
- Semantic Segmentation of Music audio Contents
- RWC Music Database: Popular, Classical and Jazz Music Databases
- Automatic Audio Segmentation: Segment Boundary and Structure Detection in Popular Music
- A Regularity-Constrained Viterbi Algorithm and Its Application to The Structural Segmentation of Songs
- UNDERTOW: Multi-Level Segmentation of Real-Valued Time Series
- Online discovery and maintenance of time series motifs
- Segmenting time series with a hybrid neural networks - hidden Markov model
- The music information retrieval evaluation exchange (2005-2007): A window into music information retrieval research
- Structural Segmentation of Musical Audio by Constrained Clustering
- Content-Based Music Information Retrieval: Current Directions and Future Challenges
- Working memory: looking back and looking forward
- Recurrence plots for the analysis of complex systems
- Smoothing Methods in Statistics
- Music Structure Analysis Using a Probabilistic Fitness Measure and a Greedy Search Algorithm
- Toward Unsupervised Activity Discovery Using Multi-Dimensional Motif Detection in Time Series
- A chorus section detection method for musical audio signals and its application to a music listening station
- Signal Processing for Music Analysis
- Cross recurrence quantification for cover song identification
- Tonal Description of Polyphonic Audio for Music Content Processing
Cited by
- Structure-Based Audio Fingerprinting for Music Retrieval
- Signal processing methods for beat tracking, music segmentation, and audio retrieval
- Cognition-inspired Descriptors for Scalable Cover Song Retrieval
- A multi-dimensional meter-adaptive method for automatic segmentation of music
- Unsupervised music segmentation via multi-scale processing of compressive features' representation
- Music segment similarity using 2D-Fourier Magnitude Coefficients
- Octave-dependent Probabilistic Latent Semantic Analysis to Chorus Detection of Popular Song
- Unsupervised Music Structure Annotation by Time Series Structure Features and Segment Similarity
- Automatic melodic and structural analysis of music material for enriched concert related experiences
- Learning to segment songs with ordinal linear discriminant analysis
- Multiple hypotheses at multiple scales for audio novelty computation within music
- Music structure analysis using self-similarity matrix and two-stage categorization
- Popular song summarization using chorus section detection from audio signal
- librosa: Audio and Music Signal Analysis in Python
- Music boundary detection using neural networks on spectrograms and self-similarity lag matrices
- Segmentation and Timbre Similarity in Electronic Dance Music
- Segmentation and timbre- and rhythm similarity in Electronic Dance Music
- Mobile Audio Intelligence: From Real Time Segmentation to Crowd Sourced Semantics
- Discovering Structure in Music: Automatic Approaches and Perceptual Evaluations
- Probabilistic Segmentation of Folk Music Recordings
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