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
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We introduce a dataset for facilitating audio-visual analysis of music performances. The dataset comprises 44 simple multi-instrument classical music pieces assembled from coordinated but separately recorded performances of individual tracks. For each piece, we provide the musical score in MIDI format, the audio recordings of the individual tracks, the audio and video recording of the assembled mixture, and ground-truth annotation files including frame-level and note-level transcriptions. We describe our methodology for the creation of the dataset, particularly highlighting our approaches to address the challenges involved in maintaining synchronization and expressiveness. We demonstrate the high quality of synchronization achieved with our proposed approach by comparing the dataset with existing widely used music audio datasets. We anticipate that the dataset will be useful for the development and evaluation of existing music information retrieval (MIR) tasks, as well as for novel multimodal tasks. We benchmark two existing MIR tasks (multipitch analysis and score-informed source separation) on the dataset and compare them with other existing music audio datasets. In addition, we consider two novel multimodal MIR tasks (visually informed multipitch analysis and polyphonic vibrato analysis) enabled by the dataset and provide evaluation measurements and baseline systems for future comparisons (from our recent work). Finally, we propose several emerging research directions that the dataset enables. 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. We propose a new approach to instrument recognition in the context of real music orchestrations ranging from solos to quartets. The strength of our approach is that it does not require prior musical source separation. Thanks to a hierarchical clustering algorithm exploiting robust probabilistic distances, we obtain a taxonomy of musical ensembles which is used to efficiently classify possible combinations of instruments played simultaneously. Moreover, a wide set of acoustic features is studied including some new proposals. In particular, signal to mask ratios are found to be useful features for audio classification. This study focuses on a single music genre (i.e., jazz) but combines a variety of instruments among which are percussion and singing voice. Using a varied database of sound excerpts from commercial recordings, we show that the segmentation of music with respect to the instruments played can be achieved with an average accuracy of 53 . 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.