Automatic structure detection for popular music

Maddage, Namunu C.

doi:10.1109/mmul.2006.3

Cited by 26 publications

(18 citation statements)

References 16 publications

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“…We consider frequencies between 60 Hz and 1000 Hz, which correspond to midi notes from B1 to B5. The upper limit is set to 1 kHz because the fundamentals and harmonics of the music notes in popular music are usually stronger than the non-harmonic components up to 1 kHz [24]. This choice is also supported by the fact that many of the higher harmonics, which are whole number multiples of the fundamental frequency, are far from any note of the Western chromatic scale.…”

Section: ) Parametersmentioning

confidence: 99%

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Joint Estimation of Chords and Downbeats From an Audio Signal

Papadopoulos

Peeters

2011

IEEE Trans. Audio Speech Lang. Process.

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Abstract-We present a new technique for joint estimation of the chord progression and the downbeats from an audio file. Musical signals are highly structured in terms of harmony and rhythm. In this paper, we intend to show that integrating knowledge of mutual dependencies between chords and metric structure allows us to enhance the estimation of these musical attributes. For this, we propose a specific topology of hidden Markov models that enables modelling chord dependence on metric structure. This model allows us to consider pieces with complex metric structures such as beat addition, beat deletion or changes in the meter. The model is evaluated on a large set of popular music songs from the Beatles that present various metric structures. We compare a semi-automatic model in which the beat positions are annotated, with a fully automatic model in which a beat tracker is used as a front-end of the system. The results show that the downbeat positions of a music piece can be estimated in terms of its harmonic structure and that conversely the chord progression estimation benefits from considering the interaction between the metric and the harmonic structures.

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Section: ) Parametersmentioning

confidence: 99%

“…At each time instant t m ,w ec o m p u t et h ec o s i n ed i s t a n c e s between the observation vector O(t m ) and each of the 24 chord templates CT i ,i ∈ [1,24].…”

Section: Chord Symbol Probabilities Computationmentioning

confidence: 99%

Joint Estimation of Chords and Downbeats From an Audio Signal

Papadopoulos

Peeters

2011

IEEE Trans. Audio Speech Lang. Process.

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“…It is based on the premise that some chord combinations are more common around structural boundaries, especially when expressed as relative chords in a key, giving a musicologically richer representation that is made possible by the concurrent estimation of keys and chords. In that regard, it is more similar to previous work by Maddage [5] or by Lee [3], only do their systems work sequentially because the chord and key estimates are used as inputs to the structure estimation, whereas our model generates them concurrently.…”

Section: Introductionmentioning

confidence: 81%

Segmenting music through the joint estimation of keys, chords and structural boundaries

Pauwels

Peeters

2013

Proceedings of the 21st ACM International Conference on Multimedia

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In this paper, we introduce a new approach to music structure segmentation that is based on the joint estimation of structural segments, keys and chords in one probabilistic framework. More precisely, the boundaries of a structure segment are determined by detecting key changes and by utilizing the difference in prior probability of chord transitions according to their position in a structural segment. In contrast to many of the recent approaches to structural segmentation, this system does not work with self-similarity matrices, although it has been designed to integrate this kind of approach into the framework at a later stage. However, just the current version of the system, using only the estimated harmony, is already producing encouraging results, especially with respect to the precise localization of the boundaries.

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“…Additional modeling constraints or auxiliary information can further improve chord labeling accuracy. These include the prior identification of the fundamental frequency or root note of each chord before the chromagram is estimated [31], information about the metrical structure [32], information about the musical structure [33], or the musical context [30]. Current state-of-the-art chord labeling programs from audio have reached an identification accuracy of up to 80% as measured by the time overlap between predicted and ground truth chord labels; see [34].…”

Section: Automatic Chord Labelingmentioning

confidence: 99%

Towards Cross-Version Harmonic Analysis of Music

Ewert

Müller

Konz

et al. 2012

IEEE Trans. Multimedia

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Abstract-For a given piece of music, there often exist multiple versions belonging to the symbolic (e.g., MIDI representations), acoustic (audio recordings), or visual (sheet music) domain. Each type of information allows for applying specialized, domain-specific approaches to music analysis tasks. In this paper, we formulate the idea of a cross-version analysis for comparing and/or combining analysis results from different representations. As an example, we realize this idea in the context of harmonic analysis to automatically evaluate MIDI-based chord labeling procedures using annotations given for corresponding audio recordings. To this end, one needs reliable synchronization procedures that automatically establish the musical relationship between the multiple versions of a given piece. This becomes a hard problem when there are significant local deviations in these versions. We introduce a novel late-fusion approach that combines different alignment procedures in order to identify reliable parts in synchronization results. Then, the cross-version comparison of the various chord labeling results is performed only on the basis of the reliable parts. Finally, we show how inconsistencies in these results across the different versions allow for a quantitative and qualitative evaluation, which not only indicates limitations of the employed chord labeling strategies but also deepens the understanding of the underlying music material.

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Automatic structure detection for popular music

Cited by 26 publications

References 16 publications

Joint Estimation of Chords and Downbeats From an Audio Signal

Joint Estimation of Chords and Downbeats From an Audio Signal

Segmenting music through the joint estimation of keys, chords and structural boundaries

Towards Cross-Version Harmonic Analysis of Music

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