A Comparative Study of Neural Models for Polyphonic Music Sequence Transduction

Automatic transcription of polyphonic music remains a challenging task in the field of Music Information Retrieval. One under-investigated point is the post-processing of time-pitch posteriograms into binary piano rolls. In this study, we investigate this task using a variety of neural network models and training procedures. We introduce an adversarial framework, that we compare against more traditional training losses. We also propose the use of binary neuron outputs and compare them to the usual real-valued outputs in both training frameworks. This allows us to train networks directly using the F-measure as training objective. We evaluate these methods using two kinds of transduction networks and two different multi-pitch detection systems, and compare the results against baseline note-tracking methods on a dataset of classical piano music. Analysis of results indicates that (1) convolutional models improve results over baseline models, but no improvement is reported for recurrent models; (2) supervised losses are superior to adversarial ones; (3) binary neurons do not improve results; (4) cross-entropy loss results in better or equal performance compared to the F-measure loss

An End-to-End Neural Network for Polyphonic Music Transcription

We present a neural network model for polyphonic music transcription. The architecture of the proposed model is analogous to speech recognition systems and comprises an acoustic model and a music language mode}. The acoustic model is a neural network used for estimating the probabilities of pitches in a frame of audio. The language model is a recurrent neural network that models the correlations between pitch combinations over time. The proposed model is general and can be used to transcribe polyphonic music without imposing any constraints on the polyphony or the number or type of instruments. The acoustic and language model predictions are combined using a probabilistic graphical model. Inference over the output variables is performed using the beam search algorithm. We investigate various neural network architectures for the acoustic models and compare their performance to two popular state-of-the-art acoustic models. We also present an efficient variant of beam search that improves performance and reduces run-times by an order of magnitude, making the model suitable for real-time applications. We evaluate the model's performance on the MAPS dataset and show that the proposed model outperforms state-of-the-art transcription systems

An End-to-End Neural Network for Polyphonic Piano Music Transcription

We present a supervised neural network model for polyphonic piano music transcription. The architecture of the proposed model is analogous to speech recognition systems and comprises an acoustic model and a music language model. The acoustic model is a neural network used for estimating the probabilities of pitches in a frame of audio. The language model is a recurrent neural network that models the correlations between pitch combinations over time. The proposed model is general and can be used to transcribe polyphonic music without imposing any constraints on the polyphony. The acoustic and language model predictions are combined using a probabilistic graphical model. Inference over the output variables is performed using the beam search algorithm. We perform two sets of experiments. We investigate various neural network architectures for the acoustic models and also investigate the effect of combining acoustic and music language model predictions using the proposed architecture. We compare performance of the neural network based acoustic models with two popular unsupervised acoustic models. Results show that convolutional neural network acoustic models yields the best performance across all evaluation metrics. We also observe improved performance with the application of the music language models. Finally, we present an efficient variant of beam search that improves performance and reduces run-times by an order of magnitude, making the model suitable for real-time applications

Learning and Evaluation Methodologies for Polyphonic Music Sequence Prediction with LSTMs

Music language models (MLMs) play an important role for various music signal and symbolic music processing tasks, such as music generation, symbolic music classification, or automatic music transcription (AMT). In this paper, we investigate Long Short-Term Memory (LSTM) networks for polyphonic music prediction, in the form of binary piano rolls. A preliminary experiment, assessing the influence of the timestep of piano rolls on system performance, highlights the need for more musical evaluation metrics. We introduce a range of metrics, focusing on temporal and harmonic aspects. We propose to combine them into a parametrisable loss to train our network. We then conduct a range of experiments with this new loss, both for polyphonic music prediction (intrinsic evaluation) and using our predictive model as a language model for AMT (extrinsic evaluation). Intrinsic evaluation shows that tuning the behaviour of a model is possible by adjusting loss parameters, with consistent results across timesteps. Extrinsic evaluation shows consistent behaviour across timesteps in terms of precision and recall with respect to the loss parameters, leading to an improvement in AMT performance without changing the complexity of the model. In particular, we show that intrinsic performance (in terms of cross entropy) is not related to extrinsic performance, highlighting the importance of using custom training losses for each specific application. Our model also compares favourably with previously proposed MLMs

Neural Networks for Analysing Music and Environmental Audio

PhDIn this thesis, we consider the analysis of music and environmental audio recordings with neural networks. Recently, neural networks have been shown to be an effective family of models for speech recognition, computer vision, natural language processing and a number of other statistical modelling problems. The composite layer-wise structure of neural networks allows for flexible model design, where prior knowledge about the domain of application can be used to inform the design and architecture of the neural network models. Additionally, it has been shown that when trained on sufficient quantities of data, neural networks can be directly applied to low-level features to learn mappings to high level concepts like phonemes in speech and object classes in computer vision. In this thesis we investigate whether neural network models can be usefully applied to processing music and environmental audio. With regards to music signal analysis, we investigate 2 different problems. The fi rst problem, automatic music transcription, aims to identify the score or the sequence of musical notes that comprise an audio recording. We also consider the problem of automatic chord transcription, where the aim is to identify the sequence of chords in a given audio recording. For both problems, we design neural network acoustic models which are applied to low-level time-frequency features in order to detect the presence of notes or chords. Our results demonstrate that the neural network acoustic models perform similarly to state-of-the-art acoustic models, without the need for any feature engineering. The networks are able to learn complex transformations from time-frequency features to the desired outputs, given sufficient amounts of training data. Additionally, we use recurrent neural networks to model the temporal structure of sequences of notes or chords, similar to language modelling in speech. Our results demonstrate that the combination of the acoustic and language model predictions yields improved performance over the acoustic models alone. We also observe that convolutional neural networks yield better performance compared to other neural network architectures for acoustic modelling. For the analysis of environmental audio recordings, we consider the problem of acoustic event detection. Acoustic event detection has a similar structure to automatic music and chord transcription, where the system is required to output the correct sequence of semantic labels along with onset and offset times. We compare the performance of neural network architectures against Gaussian mixture models and support vector machines. In order to account for the fact that such systems are typically deployed on embedded devices, we compare performance as a function of the computational cost of each model. We evaluate the models on 2 large datasets of real-world recordings of baby cries and smoke alarms. Our results demonstrate that the neural networks clearly outperform the other models and they are able to do so without incurring a heavy computation cost

Auditory Scene Analysis: The Sweet Music of Ambiguity

In this review paper aimed at the non-specialist, we explore the use that neuroscientists and musicians have made of perceptual illusions based on ambiguity. The pivotal issue is auditory scene analysis (ASA), or what enables us to make sense of complex acoustic mixtures in order to follow, for instance, a single melody in the midst of an orchestra. In general, ASA uncovers the most likely physical causes that account for the waveform collected at the ears. However, the acoustical problem is ill-posed and it must be solved from noisy sensory input. Recently, the neural mechanisms implicated in the transformation of ambiguous sensory information into coherent auditory scenes have been investigated using so-called bistability illusions (where an unchanging ambiguous stimulus evokes a succession of distinct percepts in the mind of the listener). After reviewing some of those studies, we turn to music, which arguably provides some of the most complex acoustic scenes that a human listener will ever encounter. Interestingly, musicians will not always aim at making each physical source intelligible, but rather express one or more melodic lines with a small or large number of instruments. By means of a few musical illustrations and by using a computational model inspired by neuro-physiological principles, we suggest that this relies on a detailed (if perhaps implicit) knowledge of the rules of ASA and of its inherent ambiguity. We then put forward the opinion that some degree perceptual ambiguity may participate in our appreciation of music

A computational framework for sound segregation in music signals

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200

Modelling the perception and composition of Western musical harmony.

PhD ThesisHarmony is a fundamental structuring principle in Western music, determining how simultaneously occurring musical notes combine to form chords, and how successions of chords combine to form chord progressions. Harmony is interesting to psychologists because it unites many core features of auditory perception and cognition, such as pitch perception, auditory scene analysis, and statistical learning. A current challenge is to formalise our psychological understanding of harmony through computational modelling. Here we detail computational studies of three core dimensions of harmony: consonance, harmonic expectation, and voice leading. These studies develop and evaluate computational models of the psychoacoustic and cognitive processes involved in harmony perception, and quantitatively model how these processes contribute to music composition. Through these studies we examine long-standing issues in music psychology, such as the relative contributions of roughness and harmonicity to consonance perception, the roles of low-level psychoacoustic and high-level cognitive processes in harmony perception, and the probabilistic nature of harmonic expectation. We also develop cognitively informed computational models that are capable of both analysing existing music and generating new music, with potential applications in computational creativity, music informatics, and music psychology. This thesis is accompanied by a collection of open-source software packages that implement the models developed and evaluated here, which we hope will support future research into the psychological foundations of musical harmony.