High resolution audio synchronization using chroma onset features

Ewert, Sebastian; Müller, Meinard; Grosche, Peter

doi:10.1109/icassp.2009.4959972

Cited by 99 publications

(103 citation statements)

References 4 publications

Supporting

Mentioning

102

Contrasting

Order By: Relevance

“…2(b). Our parameter estimation procedure starts by modifying the onset positions and durations of the model note events in M. To this end, we employ a high-resolution music synchronization approach described in [6] to align the note events with their corresponding occurrences in the audio. The procedure is based on Dynamic Time Warping (DTW) and chroma features but extends previous synchronization methods by introducing novel onset-based features to yield a higher alignment accuracy.…”

Section: Initialization and Estimation Of Note Timing Parametersmentioning

confidence: 99%

“…Our approach starts by initializing the pitch, onset and duration parameters in our model using the note events provided by the MIDI file. In the second step, we adapt the onset and duration parameters by aligning the note events with their corresponding occurrences in the audio using a high-resolution music synchronization approach [6]. In the third step, we iteratively modify parameters in our model related to the acoustic representation of a note event such that our model spectrogram approximates the audio spectrogram as accurately as possible.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Estimating note intensities in music recordings

Ewert

Müller

2011

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Self Cite

View full text Add to dashboard Cite

In this paper, we present automated methods for estimating note intensities in music recordings. Given a MIDI file (representing the score) and an audio recording (representing an interpretation) of a piece of music, our idea is to parametrize the spectrogram of the audio recording by exploiting the MIDI information and then to estimate the note intensities from the resulting model. The model is based on the idea of note-event spectrograms describing the part of a spectrogram that can be attributed to a given note event. After initializing our model with note events provided by the MIDI, we adapt all model parameters such that our model spectrogram approximates the audio spectrogram as accurately as possible. While note-wise intensity estimation is a very challenging task for general music, our experiments indicate promising results on polyphonic piano music.

show abstract

Section: Initialization and Estimation Of Note Timing Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimating note intensities in music recordings

Ewert

Müller

2011

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Self Cite

View full text Add to dashboard Cite

show abstract

“…Interestingly, DTW has been extended to align items that cannot be naturally represented as single sequences, such as polyphonic music [40] or audio [41,42]. Consequently, alignment has been also key to finding correspondences among related music items of not the same format (typically called music synchronization): it has been used for score following, the task of aligning different music representations such as audio and score or MIDI (Musical Instrument Digital Interface) [41,43]. Describing alignment's numerous MIR applications exceeds the scope of this study.…”

Section: Pairwise Alignmentmentioning

confidence: 99%

Melodic Similarity and Applications Using Biologically-Inspired Techniques

et al. 2017

View full text Add to dashboard Cite

Music similarity is a complex concept that manifests itself in areas such as Music Information Retrieval (MIR), musicological analysis and music cognition. Modelling the similarity of two music items is key for a number of music-related applications, such as cover song detection and query-by-humming. Typically, similarity models are based on intuition, heuristics or small-scale cognitive experiments; thus, applicability to broader contexts cannot be guaranteed. We argue that data-driven tools and analysis methods, applied to songs known to be related, can potentially provide us with information regarding the fine-grained nature of music similarity. Interestingly, music and biological sequences share a number of parallel concepts; from the natural sequence-representation, to their mechanisms of generating variations, i.e., oral transmission and evolution respectively. As such, there is a great potential for applying scientific methods and tools from bioinformatics to music. Stripped-down from biological heuristics, certain bioinformatics approaches can be generalized to any type of sequence. Consequently, reliable and unbiased data-driven solutions to problems such as biological sequence similarity and conservation analysis can be applied to music similarity and stability analysis. Our paper relies on such an approach to tackle a number of tasks and more notably to model global melodic similarity.

show abstract

“…This would require robust alignment methods which are able to detect transpositions and structural changes in a piece. One example of aligned multiple-instrument transcriptions is the set of syncRWC annotations 1 , which were created using the framework described in [46]. Also, an example of a multi-track dataset for transcription is the Bach10 dataset [40], for which ground-truth was creating by performing monophonic pitch tracking on the individual instrument tracks.…”

Section: Creating Training Datamentioning

confidence: 99%

Automatic music transcription: challenges and future directions

et al. 2013

View full text Add to dashboard Cite

This is the unspecified version of the paper.This version of the publication may differ from the final published version. Abstract Automatic music transcription is considered by many to be a key enabling technology in music signal processing. However, the performance of transcription systems is still significantly below that of a human expert, and accuracies reported in recent years seem to have reached a limit, although the field is still very active. In this paper we analyse limitations of current methods and identify promising directions for future research. Current transcription methods use general purpose models which are unable to capture the rich diversity found in music signals. One way to overcome the limited performance of transcription systems is to tailor algorithms to specific use-cases. Semi-automatic approaches are another way of achieving a more reliable transcription. Also, the wealth of musical scores and corresponding audio data now available are a rich potential source of training data, via forced alignment of audio to scores, but large scale utilisation of such data has yet to be attempted. Other promising approaches include the integration of information from multiple algorithms and different musical aspects. Permanent repository link

show abstract

High resolution audio synchronization using chroma onset features

Cited by 99 publications

References 4 publications

Estimating note intensities in music recordings

Estimating note intensities in music recordings

Melodic Similarity and Applications Using Biologically-Inspired Techniques

Automatic music transcription: challenges and future directions

Contact Info

Product

Resources

About