A Study On Lstm Networks For Polyphonic Music Sequence Modelling

2019

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...

Recent developments in neural networks and sequential models has produced state of the art results in eld signal processing and sequential data generation. For us, music is a pleasing sound, and everyone listens to music very frequently, but computers represent it as an sequential data and all sequential data generation model can be used to generate music. Our work focuses on generating music using LSTMs. LSTMs have good capability to remember previous data, they have memory unlike general RNNs. A good music must not be abruptly changing tones and themes, it must be consistent and for this purpose our model must remember what was generated previously. So, LSTMs are the perfect choice for this context-based data generation. We used Keras[2], an open-source software library that provides a Python interface for artificial neural networks, to create and train model. Most impressive results were produced by Multi-layered Char-RNN with LSTM Cell. The data is represented with ABC le format for easier access and better understanding. We preprocess the data to make it more robust and understandable for neural network and decode it back for human interpretation, the preprocessing algorithms and data representation is thoroughly discussed. The model used in this paper learn the sequences of polyphonic musical notes over a Stacked-Multilayered Char-RNN with LSTM cell. The model required and do have have the potential to recall past details of a musical sequence and its structure for better learning because of memory cells present in LSTM cells. The whole architecture with data ow and training and testing scores are explained.

IARJSET

Traditionally, music was treated as an analogue signal and was generated manually. In recent years, music is conspicuous to technology which can generate a suite of music automatically without any human intervention. To accomplish this task, we need to overcome some technical challenges which are discussed descriptively in this paper. A brief introduction about music and its components is provided in the paper along with the citation and analysis of related work accomplished by different authors in this domain. Main objective of this paper is to propose an algorithm which can be used to generate musical notes using Recurrent Neural Networks (RNN), principally Long Short-Term Memory (LSTM) networks. A model is designed to execute this algorithm where data is represented with the help of musical instrument digital interface (MIDI) file format for easier access and better understanding. Preprocessing of data before feeding it into the model, revealing methods to read, process and prepare MIDI files for input are also discussed. The model used in this paper is used to learn the sequences of polyphonic musical notes over a single-layered LSTM network. The model must have the potential to recall past details of a musical sequence and its structure for better learning. Description of layered architecture used in LSTM model and its intertwining connections to develop a neural network is presented in this work. This paper imparts a peek view of distributions of weights and biases in every layer of the model along with a precise representation of losses and accuracy at each step and batches. When the model was thoroughly analyzed, it produced stellar results in composing new melodies.

arXiv (Cornell University), 2016

2019

We present a framework based on neural networks to extract music scores directly from polyphonic audio in an end-to-end fashion. Most previous Automatic Music Transcription (AMT) methods seek a piano-roll representation of the pitches, that can be further transformed into a score by incorporating tempo estimation, beat tracking, key estimation or rhythm quantization. Unlike these methods, our approach generates music notation directly from the input audio in a single stage. For this, we use a Convolutional Recurrent Neural Network (CRNN) with Connectionist Temporal Classification (CTC) loss function which does not require annotated alignments of audio frames with the score rhythmic information. We trained our model using as input Haydn, Mozart, and Beethoven string quartets and Bach chorales synthesized with different tempos and expressive performances. The output is a textual representation of four-voice music scores based on **kern format. Although the proposed approach is evaluat...

arXiv preprint arXiv:1206.6392, 2012

Abstract: We investigate the problem of modeling symbolic sequences of polyphonic music in a completely general piano-roll representation. We introduce a probabilistic model based on distribution estimators conditioned on a recurrent neural network that is able to discover temporal dependencies in high-dimensional sequences. Our approach outperforms many traditional models of polyphonic music on a variety of realistic datasets. We show how our musical language model can serve as a symbolic prior to improve the accuracy of ...

ArXiv, 2020

Automatic generation of sequences has been a highly explored field in the last years. In particular, natural language processing and automatic music composition have gained importance due to the recent advances in machine learning and Neural Networks with intrinsic memory mechanisms such as Recurrent Neural Networks. This paper evaluates different types of memory mechanisms (memory cells) and analyses their performance in the field of music composition. The proposed approach considers music theory concepts such as transposition, and uses data transformations (embeddings) to introduce semantic meaning and improve the quality of the generated melodies. A set of quantitative metrics is presented to evaluate the performance of the proposed architecture automatically, measuring the tonality of the musical compositions.

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

Over the past years music has been continually evolving through the its tempos and beats as well as it's melody. Traditionally, music is produced by a group of musicians with different instruments combining together to create a final synchronised product. In recent years, harmonies and beats were always considered to be generated manually. However, with the advent of digital technologies and software, it has become possible for machines to generate music automatically at an alarming pace. The purpose of this research is to propose a method for creating musical notes using Recurrent Neural Networks (RNN), specifically Long Short-Term Memory (LSTM) networks. To implement this algorithm, a model is created and the data is represented in the form of a percussive instrument digital interface (MIDI) file for easy access and interpretation. The process of preparing the data for input into the model is also discussed, as well as techniques for receiving, processing, and storing MIDI files for use as input.To enhance its learning capabilities, the model should be able to remember previous details of a musical sequence and its structure. This paper discusses the use of a layered architecture in the LSTM model and how its connections interweave to create a neural network.

IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2016

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 runtimes by an order of magnitude, making the model suitable for real-time applications.

The advancement in neural network and deep learning is enabling the use of these technologies in several art and other fields, and produces outcome which are similar to humans. This paper proposes a method to generate music, different than the music samples it uses as the dataset using bidirectional recurrent neural networks (RNN) with Long Short Term Memory (LSTM) cells, efficiently, by understanding the complex relation between the different notes, pitches, timbre values, that constitutes music. Experiments with the Pokémon midi files showed that we can achieve a high performance in music generation using bidirectional recurrent neural networks with Long Short Term Memory (LSTM) cells. Further, the performance of the model with the variation in the number of epochs used is analyzed