Deep Learning Methods for Instrument Separation and Recognition

This thesis explores deep learning methods for timbral information processing in polyphonic music analysis. It encompasses two primary tasks: Music Source Separation (MSS) and Instrument Recognition, with focus on applying domain knowledge and utilising dense arrangements of skip-connections in the frameworks in order to reduce the number of trainable parameters and create more efficient models. Musically-motivated Convolutional Neural Network (CNN) architectures are introduced, emphasizing kernels with vertical, square, and horizontal shapes. This design choice allows for the extraction of essential harmonic and percussive features, which enhances the discrimination of different instruments. Notably, this methodology proves valuable for Harmonic-Percussive Source Separation (HPSS) and instrument recognition tasks. A significant challenge in MSS is generalising to new instrument types and music styles. To address this, a versatile framework for adversarial unsupervised domain adaptation for source separation is proposed, particularly beneficial when labeled data for specific instruments is unavailable. The curation of the Tap & Fiddle dataset is another contribution of the research, offering mixed and isolated stem recordings of traditional Scandinavian fiddle tunes, along with foot-tapping accompaniments, fostering research in source separation and metrical expression analysis within these musical styles. Since our perception of timbre is affected in different ways by transient and stationary parts of sound, the research investigates the potential of Transient Stationary-Noise Decomposition (TSND) as a preprocessing step for frame-level recognition. A method that performs TSND of spectrograms and feeds the decomposed spectrograms to a neural classifier is proposed. Furthermore, this thesis introduces a novel deep learning-based approach for pitch streaming, treating the task as a note-level instrument classification. Such an approach is modular, meaning that it can also successfully stream predicted note-events and not only labelled ground truth note-event information to corresponding instruments. Therefore, the proposed pitch streaming method enables third-party multi-pitch estimation algorithms to perform multi-instrument AMT

Adversarial Unsupervised Domain Adaptation for Harmonic-Percussive Source Separation

This paper addresses the problem of domain adaptation for the task of music source separation. Using datasets from two different domains, we compare the performance of a deep learning-based harmonic-percussive source separation model under different training scenarios, including supervised joint training using data from both domains and pre-training in one domain with fine-tuning in another. We propose an adversarial unsupervised domain adaptation approach suitable for the case where no labelled data (ground-truth source signals) from a target domain is available. By leveraging unlabelled data (only mixtures) from this domain, experiments show that our framework can improve separation performance on the new domain without losing any considerable performance on the original domain. The paper also introduces the Tap & Fiddle dataset, a dataset containing recordings of Scandinavian fiddle tunes along with isolated tracks for "foot-tapping" and "violin"

Pitch-informed instrument assignment using a deep convolutional network with multiple kernel shapes

This paper proposes a deep convolutional neural network for performing note-level instrument assignment. Given a polyphonic multi-instrumental music signal along with its ground truth or predicted notes, the objective is to assign an instrumental source for each note. This problem is addressed as a pitch-informed classification task where each note is analysed individually. We also propose to utilise several kernel shapes in the convolutional layers in order to facilitate learning of timbre-discriminative feature maps. Experiments on the MusicNet dataset using 7 instrument classes show that our approach is able to achieve an average F-score of 0.904 when the original multi-pitch annotations are used as the pitch information for the system, and that it also excels if the note information is provided using third-party multi-pitch estimation algorithms. We also include ablation studies investigating the effects of the use of multiple kernel shapes and comparing different input representations for the audio and the note-related information

Levels of digestible isoleucine on performance, carcass traits and organs weight of gilts (15 - 30 KG) Níveis de isoleucina digestível sobre o desempenho, características de carcaça e peso de órgãos de fêmeas suínas (15 - 30 kg)

The ideal protein concept for pigs has allowed reducing levels of crude protein in the diet, since synthetic amino acids are included because the branched chain amino acids may be limiting. In order to determine the digestible isoleucine requirement for gilts from 15 to 30 kg, a performance assay was accomplished, using 40 crossbreed gilts of high genetic potential, averaging 15.00 ± 0.52 kg of body weight, alloted in a randomized blocks design, consisting of five treatments (0.45, 0.52, 0.59, 0.66, 0.73% of digestible isoleucine), four replicates and two animals per experimental unit. Performance traits were determined and at the end of the experiment one animal per experimental unit was slaughtered to determine carcass composition and organs weight. Levels from 0.45 to 0.73% of digestible isoleucine did not affect the carcass traits and organs weight of gilts from 15 to 30 kg. A quadratic effect (P<0.05) of digestible isoleucine levels on isoleucine efficiency for weight gain was observed, that increased up to 0.506% digestible isoleucine, which ratio of digestible isoleucine: lysine was 0.51.<br>O conceito de proteína ideal, para suínos, tem permitido reduzir os níveis de proteína bruta da dieta, desde que haja a inclusão de aminoácidos sintéticos, pois os aminoácidos de cadeia ramificada podem ser limitantes. Com o objetivo de determinar a exigência de isoleucina digestível para fêmeas suínas dos 15 aos 30 kg, foi realizado um ensaio de desempenho, utilizando-se 40 fêmeas suínas, mestiças de alto potencial genético, com peso vivo inicial de 15,00 ± 0,52kg, distribuídas em um delineamento experimental de blocos ao acaso, constituído de cinco tratamentos (0,45; 0,52; 0,59; 0,66; 0,73% de isoleucina digestível), quatro repetições e dois animais por unidade experimental. Foram determinadas características de desempenho e, ao final do experimento, um animal de cada unidade experimental foi abatido, para a determinação da composição de carcaça e peso de órgãos. Níveis de 0,45 a 0,73% de isoleucina digestível não influenciaram as características de carcaça e peso de órgãos dos animais. Houve efeito quadrático (P<0,05) dos níveis de isoleucina digestível sobre a eficiência de utilização de isoleucina para ganho de peso, com aumento até o nível 0,506%; cuja relação isoleucina:lisina digestível foi de 0,51