Learning Distributed Representations for Multiple-Viewpoint Melodic Prediction

The analysis of sequences is important for extracting in- formation from music owing to its fundamentally temporal nature. In this paper, we present a distributed model based on the Restricted Boltzmann Machine (RBM) for learning melodic sequences. The model is similar to a previous suc- cessful neural network model for natural language [2]. It is first trained to predict the next pitch in a given pitch se- quence, and then extended to also make use of information in sequences of note-durations in monophonic melodies on the same task. In doing so, we also propose an efficient way of representing this additional information that takes advantage of the RBM’s structure. Results show that this RBM-based prediction model performs better than previ- ously evaluated n-gram models and also outperforms them in certain cases. It is able to make use of information present in longer sequences more effectively than n-gram models, while scaling linearly in the number of free pa- rameters required

The Recurrent Temporal Discriminative Restricted Boltzmann Machines

Classification of sequence data is the topic of interest for dynamic Bayesian models and Recurrent Neural Networks (RNNs). While the former can explicitly model the temporal dependencies between class variables, the latter have a capability of learning representations. Several attempts have been made to improve performance by combining these two approaches or increasing the processing capability of the hidden units in RNNs. This often results in complex models with a large number of learning parameters. In this paper, a compact model is proposed which offers both representation learning and temporal inference of class variables by rolling Restricted Boltzmann Machines (RBMs) and class variables over time. We address the key issue of intractability in this variant of RBMs by optimising a conditional distribution, instead of a joint distribution. Experiments reported in the paper on melody modelling and optical character recognition show that the proposed model can outperform the state-of-the-art. Also, the experimental results on optical character recognition, part-of-speech tagging and text chunking demonstrate that our model is comparable to recurrent neural networks with complex memory gates while requiring far fewer parameters

A Distributed Model For Multiple-Viewpoint Melodic Prediction.

The analysis of sequences is important for extracting information from music owing to its fundamentally temporal nature. In this paper, we present a distributed model based on the Restricted Boltzmann Machine (RBM) for melodic sequences. The model is similar to a previous successful neural network model for natural language [2]. It is first trained to predict the next pitch in a given pitch sequence, and then extended to also make use of information in sequences of note-durations in monophonic melodies on the same task. In doing so, we also propose an efficient way of representing this additional information that takes advantage of the RBM’s structure. In our evaluation, this RBM-based prediction model performs slightly better than previously evaluated n-gram models in most cases. Results on a corpus of chorale and folk melodies showed that it is able to make use of information present in longer contexts more effectively than n-gram models, while scaling linearly in the number of free parameters required

Generalising the Discriminative Restricted Boltzmann Machine

We present a novel theoretical result that generalises the Discriminative Restricted Boltzmann Machine (DRBM). While originally the DRBM was defined assuming the {0, 1}-Bernoulli distribution in each of its hidden units, this result makes it possible to derive cost functions for variants of the DRBM that utilise other distributions, including some that are often encountered in the literature. This is illustrated with the Binomial and {-1, +1}-Bernoulli distributions here. We evaluate these two DRBM variants and compare them with the original one on three benchmark datasets, namely the MNIST and USPS digit classification datasets, and the 20 Newsgroups document classification dataset. Results show that each of the three compared models outperforms the remaining two in one of the three datasets, thus indicating that the proposed theoretical generalisation of the DRBM may be valuable in practice

An RNN-based Music Language Model for Improving Automatic Music Transcription

In this paper, we investigate the use of Music Language Models (MLMs) for improving Automatic Music Transcription performance. The MLMs are trained on sequences of symbolic polyphonic music from the Nottingham dataset. We train Recurrent Neural Network (RNN)-based models, as they are capable of capturing complex temporal structure present in symbolic music data. Similar to the function of language models in automatic speech recognition, we use the MLMs to generate a prior probability for the occurrence of a sequence. The acoustic AMT model is based on probabilistic latent component analysis, and prior information from the MLM is incorporated into the transcription framework using Dirichlet priors. We test our hybrid models on a dataset of multiple-instrument polyphonic music and report a significant 3% improvement in terms of F-measure, when compared to using an acoustic-only model

Automatic Phrase Continuation from Guitar and Bass guitar Melodies

A framework is proposed for generating interesting, musically similar variations of a given monophonic melody. The focus is on pop/rock guitar and bass guitar melodies with the aim of eventual extensions to other instruments and musical styles. It is demonstrated here how learning musical style from segmented audio data can be formulated as an unsupervised learning problem to generate a symbolic representation. A melody is first segmented into a sequence of notes using onset detection and pitch estimation. A set of hierarchical, coarse-to-fine symbolic representations of the melody is generated by clustering pitch values at multiple similarity thresholds. The variance ratio criterion is then used to select the appropriate clustering levels in the hierarchy. Note onsets are aligned with beats, considering the estimated meter of the melody, to create a sequence of symbols that represent the rhythm in terms of onsets/rests and the metrical locations of their occurrence. A joint representation based on the cross-product of the pitch cluster indices and metrical locations is used to train the prediction model, a variable-length Markov chain. The melodies generated by the model were evaluated through a questionnaire by a group of experts, and received an overall positive response. </jats:p

Malodorous consequences: What comprises negligence in anosmia litigation?

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106718/1/alr21257.pd