Publications - Mickael Zehren

Journal Article

Automatic Detection of Cue Points for the Emulation of DJ Mixing
Mickael Zehren, Marco Alunno and Paolo Bientinesi
Computer Music Journal, Volume 46(3), 2023.
```
@article{Zehren2023:690,
    author    = "Mickael Zehren and Marco Alunno and Paolo Bientinesi",
    title     = "Automatic Detection of Cue Points for the Emulation of DJ Mixing",
    journal   = "Computer Music Journal",
    year      = 2023,
    volume    = 46,
    number    = 3,
    publisher = "MIT Press"
}
```
The automatic identification of cue points is a central task in applications as diverse as music thumbnailing, mash-ups generation, and DJ mixing. Our focus lies in electronic dance music and in a specific kind of cue point, the ``switch point,'' that make it possible to automatically construct transitions among tracks, mimicking what professional DJs do. We present two approaches for the detection of switch points. One embodies a few general rules we established from interviews with professional DJs; the other models a manually annotated dataset that we curated. Both approaches are based on feature extraction and novelty analysis. From an evaluation conducted on previously unknown tracks, we found that about 90\% of the points generated can be reliably used in the context of a DJ mix.
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Peer Reviewed Conference Publication

ADTOF: A large dataset of non-synthetic music for automatic drum transcription
Mickael Zehren, Marco Alunno and Paolo Bientinesi
Proceedings of the Proceedings of the 22nd International Society for Music Information Retrieval Conference, pp. 818-824, 2021.
```
@inproceedings{Zehren2021:468,
    author = "Mickael Zehren and Marco Alunno and Paolo Bientinesi",
    title  = "ADTOF: A large dataset of non-synthetic music for automatic drum transcription",
    year   = 2021,
    pages  = "818-824",
    url    = "https://arxiv.org/abs/2111.11737"
}
```
The state-of-the-art methods for drum transcription in the presence of melodic instruments (DTM) are machine learning models trained in a supervised manner, which means that they rely on labeled datasets. The problem is that the available public datasets are limited either in size or in realism, and are thus suboptimal for training purposes. Indeed, the best results are currently obtained via a rather convoluted multi-step training process that involves both real and synthetic datasets. To address this issue, starting from the observation that the communities of rhythm games players provide a large amount of annotated data, we curated a new dataset of crowdsourced drum transcriptions. This dataset contains real-world music, is manually annotated, and is about two orders of magnitude larger than any other non-synthetic dataset, making it a prime candidate for training purposes. However, due to crowdsourcing, the initial annotations contain mistakes. We discuss how the quality of the dataset can be improved by automatically correcting different types of mistakes. When used to train a popular DTM model, the dataset yields a performance that matches that of the state-of-the-art for DTM, thus demonstrating the quality of the annotations.
abstractPDFbibtexhide