High-dimensional sequence transduction

We investigate the problem of transforming an input sequence into a high-dimensional output sequence in order to transcribe polyphonic audio music into symbolic notation. We introduce a probabilistic model based on a recurrent neural network that is able to learn realistic output distributions given the input and we devise an efficient algorithm to search for the global mode of that distribution. The resulting method produces musically plausible transcriptions even under high levels of noise and drastically outperforms previous state-of-the-art approaches on five datasets of synthesized sounds and real recordings, approximately halving the test error rate

End-to-End Attention-based Large Vocabulary Speech Recognition

Many of the current state-of-the-art Large Vocabulary Continuous Speech Recognition Systems (LVCSR) are hybrids of neural networks and Hidden Markov Models (HMMs). Most of these systems contain separate components that deal with the acoustic modelling, language modelling and sequence decoding. We investigate a more direct approach in which the HMM is replaced with a Recurrent Neural Network (RNN) that performs sequence prediction directly at the character level. Alignment between the input features and the desired character sequence is learned automatically by an attention mechanism built into the RNN. For each predicted character, the attention mechanism scans the input sequence and chooses relevant frames. We propose two methods to speed up this operation: limiting the scan to a subset of most promising frames and pooling over time the information contained in neighboring frames, thereby reducing source sequence length. Integrating an n-gram language model into the decoding process yields recognition accuracies similar to other HMM-free RNN-based approaches

A Hybrid Recurrent Neural Network For Music Transcription

We investigate the problem of incorporating higher-level symbolic score-like information into Automatic Music Transcription (AMT) systems to improve their performance. We use recurrent neural networks (RNNs) and their variants as music language models (MLMs) and present a generative architecture for combining these models with predictions from a frame level acoustic classifier. We also compare different neural network architectures for acoustic modeling. The proposed model computes a distribution over possible output sequences given the acoustic input signal and we present an algorithm for performing a global search for good candidate transcriptions. The performance of the proposed model is evaluated on piano music from the MAPS dataset and we observe that the proposed model consistently outperforms existing transcription methods

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

Аналитический обзор интегральных систем распознавания речи

This article presents an analytic survey of various end-to-end speech recognition systems, as well as some approaches to their construction and optimization. We consider models based on connectionist temporal classification (CTC), models based on encoder-decoder architecture with attention mechanism and models using conditional random field (CRF). We also describe integration possibilities with language models at a stage of decoding. We see that such an approach significantly reduces recognition error rates for end-to-end models. A survey of research works in this subject area reveals that end-to-end systems allow achieving results close to that of the state-of-the-art hybrid models. Nevertheless, end-to-end models use simple configuration and demonstrate a high speed of learning and decoding. In addition, we consider popular frameworks and toolkits for creating speech recognition systems.Приведен аналитический обзор разновидностей интегральных (end-to-end) систем для распознавания речи, методов их построения, обучения и оптимизации. Рассмотрены варианты моделей на основе коннекционной временной классификации (CTC) в качестве функции потерь для нейронной сети, модели на основе механизма внимания и шифратор-дешифратор моделей. Также рассмотрены нейронные сети, построенные с использованием условных случайных полей (CRF), которые являются обобщением скрытых марковских моделей, что позволяет исправить многие недостатки стандартных гибридных систем распознавания речи, например, предположение о том, что элементы входных последовательностей звуков речи являются независимыми случайными величинами. Также описаны возможности интеграции с языковыми моделями на этапе декодирования, демонстрирующие существенное сокращение ошибки распознавания для интеграционных моделей. Описаны различные модификации и улучшения стандартных интегральных архитектур систем распознавания речи, как, например, обобщение коннекционной классификации и использовании регуляризации в моделях, основанных на механизмах внимания. Обзор исследований, проводимых в данной предметной области, показывает, что интегральные системы распознавания речи позволяют достичь результатов, сравнимых с результатами стандартных систем, использующих скрытые марковские модели, но с применением более простой конфигурации и быстрой работой системы распознавания как при обучении, так и при декодировании. Рассмотрены наиболее популярные и развивающиеся библиотеки и инструментарии для построения интегральных систем распознавания речи, такие как TensorFlow, Eesen, Kaldi и другие. Проведено сравнение описанных инструментариев по критериям простоты и доступности их использования для реализации интегральных систем распознавания речи