Known-Artist Live Song Identification Using Audio Hashprints

Tsai, TJ; Prätzlich, Thomas; Müller, Meinard

doi:10.1109/tmm.2017.2669864

Cited by 7 publications

(2 citation statements)

References 37 publications

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“…At the lower end of the specificity scale are tasks such as genre recognition [36]. A medium-level specificity is associated with tasks such as audio matching [12,14], version identification [31,37], live song detection [28,38], and cover song retrieval [8,10,13,15,16,[21][22][23][24][25][26][27]29,30]. In all of these tasks, one allows for variations as they typically occur in different performances and arrangements of a piece of music.…”

Section: Related Workmentioning

confidence: 99%

Learning Low-Dimensional Embeddings of Audio Shingles for Cross-Version Retrieval of Classical Music

Zalkow

Müller

2019

Applied Sciences

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Cross-version music retrieval aims at identifying all versions of a given piece of music using a short query audio fragment. One previous approach, which is particularly suited for Western classical music, is based on a nearest neighbor search using short sequences of chroma features, also referred to as audio shingles. From the viewpoint of efficiency, indexing and dimensionality reduction are important aspects. In this paper, we extend previous work by adapting two embedding techniques; one is based on classical principle component analysis, and the other is based on neural networks with triplet loss. Furthermore, we report on systematically conducted experiments with Western classical music recordings and discuss the trade-off between retrieval quality and embedding dimensionality. As one main result, we show that, using neural networks, one can reduce the audio shingles from 240 to fewer than 8 dimensions with only a moderate loss in retrieval accuracy. In addition, we present extended experiments with databases of different sizes and different query lengths to test the scalability and generalizability of the dimensionality reduction methods. We also provide a more detailed view into the retrieval problem by analyzing the distances that appear in the nearest neighbor search.

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Section: Related Workmentioning

confidence: 99%

Learning Low-Dimensional Embeddings of Audio Shingles for Cross-Version Retrieval of Classical Music

Zalkow

Müller

2019

Applied Sciences

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“…The work by Wang et al [14], together with a few submissions to MIREX 2015 1 is, to the best of our knowledge, the only one specifically targeting the SLI task. Although some works in the VI literature address the use case of live performance identification [15,16], the proposed approaches and evaluation contexts do not consider entire concerts nor retrieving timestamps. Therefore, one cannot consider them as examples for SLI.…”

Section: Introductionmentioning

confidence: 99%

Investigating the Efficacy of Music Version Retrieval Systems for Setlist Identification

Yesiler

Molina²,

Serrà³

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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The setlist identification (SLI) task addresses a music recognition use case where the goal is to retrieve the metadata and timestamps for all the tracks played in live music events. Due to various musical and non-musical changes in live performances, developing automatic SLI systems is still a challenging task that, despite its industrial relevance, has been under-explored in the academic literature. In this paper, we propose an end-to-end workflow that identifies relevant metadata and timestamps of live music performances using a version identification system. We compare 3 of such systems to investigate their suitability for this particular task. For developing and evaluating SLI systems, we also contribute a new dataset that contains 99.5 h of concerts with annotated metadata and timestamps, along with the corresponding reference set. The dataset is categorized by audio qualities and genres to analyze the performance of SLI systems in different use cases. Our approach can identify 68% of the annotated segments, with values ranging from 35% to 77% based on the genre. Finally, we evaluate our approach against a database of 56.8 k songs to illustrate the effect of expanding the reference set, where we can still identify 56% of the annotated segments.

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Classification of Chaotic Dynamics Through Time–Frequency Representations and Machine Learning

Platas-Garza,

Zambrano-Serrano

2023

Complex Systems and Their Applications

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Known-Artist Live Song Identification Using Audio Hashprints

Cited by 7 publications

References 37 publications

Learning Low-Dimensional Embeddings of Audio Shingles for Cross-Version Retrieval of Classical Music

Learning Low-Dimensional Embeddings of Audio Shingles for Cross-Version Retrieval of Classical Music

Investigating the Efficacy of Music Version Retrieval Systems for Setlist Identification

Classification of Chaotic Dynamics Through Time–Frequency Representations and Machine Learning

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