Visualizing music and audio using self-similarity
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 1)
Music thumbnailing via structural analysis
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
SmartMusicKIOSK: music listening station with chorus-search function
Proceedings of the 16th annual ACM symposium on User interface software and technology
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
Digital Signal Processing (4th Edition)
Digital Signal Processing (4th Edition)
Music summarization using key phrases
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 02
Audio thumbnailing of popular music using chroma-based representations
IEEE Transactions on Multimedia
Music structure analysis using a probabilistic fitness measure and a greedy search algorithm
IEEE Transactions on Audio, Speech, and Language Processing
Towards timbre-invariant audio features for harmony-based music
IEEE Transactions on Audio, Speech, and Language Processing
Determination of nonprototypical valence and arousal in popular music: features and performances
EURASIP Journal on Audio, Speech, and Music Processing - Special issue on scalable audio-content analysis
Lyrics-based audio retrieval and multimodal navigation in music collections
ECDL'07 Proceedings of the 11th European conference on Research and Advanced Technology for Digital Libraries
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One major goal of structural analysis of an audio recording is to automatically extract the repetitive structure or, more generally, the musical form of the underlying piece of music. Recent approaches to this problem work well for music, where the repetitions largely agree with respect to instrumentation and tempo, as is typically the case for popular music. For other classes of music such as Western classical music, however, musically similar audio segments may exhibit significant variations in parameters such as dynamics, timbre, execution of note groups, modulation, articulation, and tempo progression. In this paper, we propose a robust and efficient algorithm for audio structure analysis, which allows to identify musically similar segments even in the presence of large variations in these parameters. To account for such variations, our main idea is to incorporate invariance at various levels simultaneously: we design a new type of statistical features to absorb microvariations, introduce an enhanced local distance measure to account for local variations, and describe a new strategy for structure extraction that can cope with the global variations. Our experimental results with classical and popular music show that our algorithm performs successfully even in the presence of significant musical variations.