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MMDenseLSTM: An efficient combination of convolutional and recurrent neural networks for audio source separation
Naoya Takahashi, Nabarun Goswami, Yuki Mitsufuji
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Deep neural networks have become an indispensable technique for audio source separation (ASS). It was recently reported that a variant of CNN architecture called MMDenseNet was successfully employed to solve the ASS problem of estimating source amplitudes, and state-of-the-art results were obtained for DSD100 dataset. To further enhance MMDenseNet, here we propose a novel architecture that integrates long short-term memory (LSTM) in multiple scales with skip connections to efficiently model long-term structures within an audio context. The experimental results show that the proposed method outperforms MMDenseNet, LSTM and a blend of the two networks. The number of parameters and processing time of the proposed model are significantly less than those for simple blending. Furthermore, the proposed method yields better results than those obtained using ideal binary masks for a singing voice separation task.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Music Source Separation | MUSDB18 | TAK2 | SDR (avg) | 6.04 | #17 of 27 | Archive leaderboard | report |
| Music Source Separation | MUSDB18 | TAK2 | SDR (bass) | 5.40 | #17 of 27 | Archive leaderboard | report |
| Music Source Separation | MUSDB18 | TAK2 | SDR (drums) | 6.81 | #17 of 27 | Archive leaderboard | report |
| Music Source Separation | MUSDB18 | TAK2 | SDR (other) | 4.80 | #17 of 27 | Archive leaderboard | report |
| Music Source Separation | MUSDB18 | TAK2 | SDR (vocals) | 7.16 | #17 of 27 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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