April 2024
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26 Reads
Transactions of the International Society for Music Information Retrieval
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April 2024
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26 Reads
Transactions of the International Society for Music Information Retrieval
April 2024
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24 Reads
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1 Citation
Transactions of the International Society for Music Information Retrieval
November 2023
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9 Reads
August 2023
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145 Reads
This paper summarizes the cinematic demixing (CDX) track of the Sound Demixing Challenge 2023 (SDX'23). We provide a comprehensive summary of the challenge setup, detailing the structure of the competition and the datasets used. Especially, we detail CDXDB23, a new hidden dataset constructed from real movies that was used to rank the submissions. The paper also offers insights into the most successful approaches employed by participants. Compared to the cocktail-fork baseline, the best-performing system trained exclusively on the simulated Divide and Remaster (DnR) dataset achieved an improvement of 1.8dB in SDR whereas the top performing system on the open leaderboard, where any data could be used for training, saw a significant improvement of 5.7dB.
August 2023
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108 Reads
This paper summarizes the music demixing (MDX) track of the Sound Demixing Challenge (SDX'23). We provide a summary of the challenge setup and introduce the task of robust music source separation (MSS), i.e., training MSS models in the presence of errors in the training data. We propose a formalization of the errors that can occur in the design of a training dataset for MSS systems and introduce two new datasets that simulate such errors: SDXDB23_LabelNoise and SDXDB23_Bleeding1. We describe the methods that achieved the highest scores in the competition. Moreover, we present a direct comparison with the previous edition of the challenge (the Music Demixing Challenge 2021): the best performing system under the standard MSS formulation achieved an improvement of over 1.6dB in signal-to-distortion ratio over the winner of the previous competition, when evaluated on MDXDB21. Besides relying on the signal-to-distortion ratio as objective metric, we also performed a listening test with renowned producers/musicians to study the perceptual quality of the systems and report here the results. Finally, we provide our insights into the organization of the competition and our prospects for future editions.
May 2023
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1,049 Reads
Music demixing is the task of separating different tracks from the given single audio signal into components, such as drums, bass, and vocals from the rest of the accompaniment. Separation of sources is useful for a range of areas, including entertainment and hearing aids. In this paper, we introduce two new benchmarks for the sound source separation tasks and compare popular models for sound demixing, as well as their ensembles, on these benchmarks. For the models' assessments, we provide the leaderboard at https://mvsep.com/quality_checker/, giving a comparison for a range of models. The new benchmark datasets are available for download. We also develop a novel approach for audio separation, based on the ensembling of different models that are suited best for the particular stem. The proposed solution was evaluated in the context of the Music Demixing Challenge 2023 and achieved top results in different tracks of the challenge. The code and the approach are open-sourced on GitHub.
January 2021
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5 Reads
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1 Citation
March 2020
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38 Reads
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2 Citations
September 2019
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8 Reads
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2 Citations
September 2018
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14 Reads
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3 Citations
... Two years later, we organized a new edition of the challenge, named Sound Demixing Challenge 2023 (SDX'23): while MDX'21 focused exclusively on music source separation (MSS), SDX'23 presented two independent tracks, one for music source separation and one for cinematic sound separation (Uhlich et al., 2024). In the music track, we again used MDXDB21 as test benchmark. ...
April 2024
Transactions of the International Society for Music Information Retrieval
... Additionally, the research presents a methodology for performing the exponential operation in RNS. Both of these objectives hold fundamental importance in advancing the practical application of unconventional numerical systems, such as RNS, in pattern recognition systems and digital signal processing [11,15,16,24,26]. ...
March 2020
... For the reason of the functional options advance, including sophistication of micro electronic gadgets, the opportunity of malfunction is just an augment in the event of various physical hindrances [1]. Based on statistics, prevalent quantity of failures are within combinational circuits of automation sector [2]. Consequently, the vital task of nowadays is the synthesis of robust failsafe joint circuits as well as testing functional diagnostic systems arrangement [3 -6]. ...
September 2018
... Такой код обнаруживает такие ошибки, которые связаны с искажениями q идущих подряд разрядов (пачки). Для решения задач технической диагностики и синтеза цифровых устройств применяются различные способы кодирования [18][19][20][21]. Например, в [22] применено взвешивание произвольными весовыми коэффициентами с учетом характеристик возникающих ошибок на выходах исходного устройства. ...
January 2018
... Reconvergence fanouts are the source of error in reliability estimation methods. In studies [17,18], a bit-parallel simulation-based method has been developed that takes care of reconvergent fanouts by forward and backward propagation of pseudo-random bit streams. Observability don't-car (ODC) mask (estimated by this approach) is applied for reliability computation of the circuit's output. ...
January 2018
... In [32], Han et al. also construct a gate reliability model that derives approximate reliability results by bounding gate errors at much smaller time and space complexities than PTM. Upper and lower bounds are used in [33] to improve scalability as well, where they are used in the context of gate observability. The work of Rejimon and Bhanja [34] adopts Bayesian Networks to capture the dependencies among signals, constructing for each node of the network a function for the truth table of the gate including error probabilities. ...
January 2017