태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
This is a general task that covers transforming audio inputs into audio outputs, not limited to existing PaperWithCode categories of Source Separation, Denoising, Classification, Recognition, etc.
출처에 같은 태스크 이름으로 연결된 데이터셋입니다. 동명 태스크가 포함될 수 있으므로, 비교할 때는 평가 결과의 조건도 확인하세요.
Audio samples processed with sound effects, to evaluate effect removal models. The audio effects applied are from the set (Distortion, Delay, Dynamic Range Compressor, Phasor, Reverb) and randomly sampled without replacement for each example; the targets are the original audio. The audio samples are source from VocalSet, GuitarSet, DSD100, and IDMT-SMT-Drums.
LA-2A Compressor data to accompany the paper "SignalTrain: Profiling Audio Compressors with Deep Neural Networks," https://arxiv.org/abs/1905.11928 Accompanying computer code: https://github.com/drscotthawley/signaltrain A collection of recorded data from an analog Teletronix LA-2A opto-electronic compressor, for various settings of the Peak Reduction knob. Other knobs were kept constant. Audio samples present in these files are either 'randomly generated', or downloaded audio clips with Create Commons licenses, or are property of Scott Hawley freely distributed as part of this dataset. Data taken by Ben Colburn, supervised by Scott Hawley Dataset used in: * "Efficient neural networks for real-time analog audio effect modeling" by C. Steinmetz & J. Reiss, 2021. https://arxiv.org/abs/2102.06200 * “Exploring quality and generalizability in parameterized neural audio effects," by W. Mitchell and S. H. Hawley, 149th Audio Engineering Society Convention (AES), 2020. https://arxiv.org/abs/2006.05584 * "SignalTrain: Profiling Audio Compressors with Deep Neural Networks," 147th Audio Engineering Society Convention (AES), 2019. https://arxiv.org/abs/1905.11928
435 vocal presets retrieval from MedleyDB and a private collection of multi-track mixes.
We present a multilingual test set for conducting speech intelligibility tests in the form of diagnostic rhyme tests. The materials currently contain audio recordings in 5 languages and further extensions are in progress. For Mandarin Chinese, we provide recordings for a consonant contrast test as well as a tonal contrast test. Further information on the audio data, test procedure and software to set up a full survey which can be deployed on crowdsourcing platforms is provided in our paper [arXiv preprint] and GitHub repository. We welcome contributions to this open-source project.
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.