Comunicacio presentada a la 22a International Conference on Digital Audio Effects (DAFx-19) que se celebra del 2 al 6 de setembre de 2019 a Birmingham, Regne Unit.
Within the context of NSynth @cite_10 , a new high-quality dataset of one shot instrumental notes was presented, largely surpassing the size of the previous datasets, containing @math musical notes with unique pitch, timbre and envelope. The sounds were collected from @math instruments from commercial sample libraries and are annotated based on their source (acoustic, electronic or synthetic), instrument family and sonic qualities. The instrument families used in the annotation are bass, brass, flute, guitar, keyboard, mallet, organ, reed, string, synth lead and vocal. The dataset is available online https: magenta.tensorflow.org datasets nsynth and provides a good basis for training and evaluating one shot instrumental sound classifiers. This dataset is already split in training, validation and test set, where the instruments present in the training set do not overlap with the ones present in validation and test sets. However, to the best of our knowledge, no methods for instrument classification have so far been evaluated on this dataset.
@cite_10
2951535099
Generative models in vision have seen rapid progress due to algorithmic improvements and the availability of high-quality image datasets. In this paper, we offer contributions in both these areas to enable similar progress in audio modeling. First, we detail a powerful new WaveNet-style autoencoder model that conditions an autoregressive decoder on temporal codes learned from the raw audio waveform. Second, we introduce NSynth, a large-scale and high-quality dataset of musical notes that is an order of magnitude larger than comparable public datasets. Using NSynth, we demonstrate improved qualitative and quantitative performance of the WaveNet autoencoder over a well-tuned spectral autoencoder baseline. Finally, we show that the model learns a manifold of embeddings that allows for morphing between instruments, meaningfully interpolating in timbre to create new types of sounds that are realistic and expressive. This paper introduces a new large-scale music dataset, MusicNet, to serve as a source of supervision and evaluation of machine learning methods for music research. MusicNet consists of hundreds of freely-licensed classical music recordings by 10 composers, written for 11 instruments, together with instrument note annotations resulting in over 1 million temporal labels on 34 hours of chamber music performances under various studio and microphone conditions. The paper defines a multi-label classification task to predict notes in musical recordings, along with an evaluation protocol, and benchmarks several machine learning architectures for this task: i) learning from spectrogram features; ii) end-to-end learning with a neural net; iii) end-to-end learning with a convolutional neural net. These experiments show that end-to-end models trained for note prediction learn frequency selective filters as a low-level representation of audio. This paper introduces a new large-scale music dataset, MusicNet, to serve as a source of supervision and evaluation of machine learning methods for music research. MusicNet consists of hundreds of freely-licensed classical music recordings by 10 composers, written for 11 instruments, together with instrument note annotations resulting in over 1 million temporal labels on 34 hours of chamber music performances under various studio and microphone conditions. @PARASPLIT The paper defines a multi-label classification task to predict notes in musical recordings, along with an evaluation protocol, and benchmarks several machine learning architectures for this task: i) learning from spectrogram features; ii) end-to-end learning with a neural net; iii) end-to-end learning with a convolutional neural net. These experiments show that end-to-end models trained for note prediction learn frequency selective filters as a low-level representation of audio. In this paper, we optimize and evaluate computational models of similarity for sounds from the same instrument class. We investigate four instrument classes: bass drums, snare drums, high-pitched toms, and low-pitched toms. We evaluate two similarity models: one is defined in the ISO IEC MPEG-7 standard, and the other is based on auditory images. For the second model, we study the impact of various parameters. We use data from listening tests, and instrument class labels to evaluate the models. Our results show that the model based on auditory images yields a very high average correlation with human similarity ratings and clearly outperforms the MPEG-7 recommendation. The average correlations range from 0.89-0.96 depending on the instrument class. Furthermore, our results indicate that instrument class data can be used as alternative to data from listening tests to evaluate sound similarity models.