Discovering and exploring the underlying structure of multi-instrumental music using learning-based approaches remains an open problem. We extend the recent MusicVAE model to represent multitrack polyphonic measures as vectors in a latent space. Our approach enables several useful operations such as generating plausible measures from scratch, interpolating between measures in a musically meaningful way, and manipulating specific musical attributes. We also introduce chord conditioning, which allows all of these operations to be performed while keeping harmony fixed, and allows chords to be changed while maintaining musical "style". By generating a sequence of measures over a predefined chord progression, our model can produce music with convincing long-term structure. We demonstrate that our latent space model makes it possible to intuitively control and generate musical sequences with rich instrumentation (see this https URL for generated audio).
The MusicVAE architecture introduced by @cite_39 is able to learn a latent space of musical sequences using a novel hierarchical decoder that allows it to model long-term structure and multi-instrument sequences. However, this work applies strict constraints to the sequences to reach its goals. In order to guarantee a constant number of events per measure, non-drum tracks are limited to monophonic sequences, and all tracks are represented with a single velocity and quantized at the level of 16 th notes. This reduces the challenge of modeling longer-term structure, but at the expense of expressiveness. Furthermore, while the trio'' MusicVAE is capable of modeling three broad instrument classes--melody, bass, and drums--it is limited to exactly three instruments, arbitrarily excluding potentially pivotal voices and disregarding the specific identity of each instrument (e.g. eletric guitar and piano are both considered melody'' instruments). Further, do not consider modeling fine-grained timing and velocity, nor do they develop a method for chord conditioning. Nevertheless, the MusicVAE architecture and its implementation provide a powerful basis for exploring a more expressive and complete multitrack latent space, and thus we position our work as an extension of it.
@cite_39
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Discovering and exploring the underlying structure of multi-instrumental music using learning-based approaches remains an open problem. We extend the recent MusicVAE model to represent multitrack polyphonic measures as vectors in a latent space. Our approach enables several useful operations such as generating plausible measures from scratch, interpolating between measures in a musically meaningful way, and manipulating specific musical attributes. We also introduce chord conditioning, which allows all of these operations to be performed while keeping harmony fixed, and allows chords to be changed while maintaining musical "style". By generating a sequence of measures over a predefined chord progression, our model can produce music with convincing long-term structure. We demonstrate that our latent space model makes it possible to intuitively control and generate musical sequences with rich instrumentation (see this https URL for generated audio). The Variational Autoencoder (VAE) has proven to be an effective model for producing semantically meaningful latent representations for natural data. However, it has thus far seen limited application to sequential data, and, as we demonstrate, existing recurrent VAE models have difficulty modeling sequences with long-term structure. To address this issue, we propose the use of a hierarchical decoder, which first outputs embeddings for subsequences of the input and then uses these embeddings to generate each subsequence independently. This structure encourages the model to utilize its latent code, thereby avoiding the "posterior collapse" problem which remains an issue for recurrent VAEs. We apply this architecture to modeling sequences of musical notes and find that it exhibits dramatically better sampling, interpolation, and reconstruction performance than a "flat" baseline model. An implementation of our "MusicVAE" is available online at this http URL Machine learning has shown a successful component of methods for automatic music composition. Considering music as a sequence of events with multiple complex dependencies on various levels of a composition, the long short-term memory-based (LSTM) architectures have been proven to be very efficient in learning and reproducing musical styles. The “rampant force” of these architectures, however, makes them hardly useful for tasks that incorporate human input or generally constraints. Such an example is the generation of drums’ rhythms under a given metric structure (potentially combining different time signatures), with a given instrumentation (e.g. bass and guitar notes). This paper presents a solution that harnesses the LSTM sequence learner with a feed-forward (FF) part which is called the “Conditional Layer”. The LSTM and the FF layers influence (are merged into) a single layer making the final decision about the next drums’ event, given previous events (LSTM layer) and current constraints (FF layer). The resulting architecture is called the conditional neural sequence learner (CNSL). Results on drums’ rhythm sequences are presented indicating that the CNSL architecture is effective in producing drums’ sequences that resemble a learnt style, while at the same time conform to given constraints; impressively, the CNSL is able to compose drums’ rhythms in time signatures it has not encountered during training (e.g. 17 16), which resemble the characteristics of the rhythms in the original data. This paper presents a probabilistic model of temporal structure in music which allows joint inference of tempo, meter and rhythmic pattern. The framework of the model naturally quantifies these three musical concepts in terms of hidden state-variables, allowing resolution of otherwise apparent ambiguities in musical structure. At the heart of the system is a probabilistic model of a hypothetical ‘bar-pointer’ which maps an input signal to one cycle of a latent, periodic rhythmical pattern. The system flexibly accommodates different input signals via two observation models: a Poisson points model for use with MIDI onset data and a Gaussian process model for use with raw audio signals. The discrete state-space permits exact computation of posterior probability distributions for the quantities of interest. Results are presented for both observation models, demonstrating the ability of the system to correctly detect changes in rhythmic pattern and meter, whilst tracking tempo. The temporal structure of music is essential for the cognitive processes related to the emotions expressed in music. However, such temporal information is often disregarded in typical Music Information Retrieval modeling tasks of predicting higher-level cognitive or semantic aspects of music such as emotions, genre, and similarity. This paper addresses the specific hypothesis whether temporal information is essential for predicting expressed emotions in music, as a prototypical example of a cognitive aspect of music. We propose to test this hypothesis using a novel processing pipeline: 1) Extracting audio features for each track resulting in a multivariate "feature time series". 2) Using generative models to represent these time series (acquiring a complete track representation). Specifically, we explore the Gaussian Mixture model, Vector Quantization, Autoregressive model, Markov and Hidden Markov models. 3) Utilizing the generative models in a discriminative setting by selecting the Probability Product Kernel as the natural kernel for all considered track representations. We evaluate the representations using a kernel based model specifically extended to support the robust two-alternative forced choice self-report paradigm, used for eliciting expressed emotions in music. The methods are evaluated using two data sets and show increased predictive performance using temporal information, thus supporting the overall hypothesis. We present a probabilistic generative model for timing deviations in expressive music performance. The structure of the proposed model is equivalent to a switching state space model. The switch variables correspond to discrete note locations as in a musical score. The continuous hidden variables denote the tempo. We formulate two well known music recognition problems, namely tempo tracking and automatic transcription (rhythm quantization) as filtering and maximum a posteriori (MAP) state estimation tasks. Exact computation of posterior features such as the MAP state is intractable in this model class, so we introduce Monte Carlo methods for integration and optimization. We compare Markov Chain Monte Carlo (MCMC) methods (such as Gibbs sampling, simulated annealing and iterative improvement) and sequential Monte Carlo methods (particle filters). Our simulation results suggest better results with sequential methods. The methods can be applied in both online and batch scenarios such as tempo tracking and transcription and are thus potentially useful in a number of music applications such as adaptive automatic accompaniment, score typesetting and music information retrieval. Machine improvisation and related style simulation problems usually consider building repre- sentations of time-based media data, such as music, either by explicit coding of rules or applying machine learning methods. Stylistic learning applies such methods to musical sequences in order to capture salient musical features and organize these features into a model. The Stylistic simulation process browses the model in order to generate variant musical sequences that are stylistically consistent with the learned ma- terial. If both the learning process and the simulation process happen in real-time, in an interactive system where the computer “plays” with musicians, then Machine Improvisation is achieved. Improvisation models have to cope with a trade-off between completeness (all the possible patterns and their continuation laws are discovered) and incrementality (the completeness is ensured only asymptotically for infinite sequences). In a previous work we devised a complete and incremental model based on the Factor Oracle Algorithm. In this paper we propose a concurrent constraints model for the Factor Oracle and show how it can be used in a concurrent learning improvisation situation. Our model is based on a non-deterministic concurrent constraint process calculus (NTCC). Such an approach allows the system to respond in a faster and more flexible manner to real-life performance situations. In addition, the declarative nature of constraints greatly simplifies the expansion of the system with improvisation rules at a higher musical level. We also describe the implementation of our model in a NTCC interpreter written in Common Lisp that is capable of real time performance. We present a VAE architecture for encoding and generating high dimensional sequential data, such as video or audio. Our deep generative model learns a latent representation of the data which is split into a static and dynamic part, allowing us to approximately disentangle latent time-dependent features (dynamics) from features which are preserved over time (content). This architecture gives us partial control over generating content and dynamics by conditioning on either one of these sets of features. In our experiments on artificially generated cartoon video clips and voice recordings, we show that we can convert the content of a given sequence into another one by such content swapping. For audio, this allows us to convert a male speaker into a female speaker and vice versa, while for video we can separately manipulate shapes and dynamics. Furthermore, we give empirical evidence for the hypothesis that stochastic RNNs as latent state models are more efficient at compressing and generating long sequences than deterministic ones, which may be relevant for applications in video compression. We introduce a dataset for facilitating audio-visual analysis of music performances. The dataset comprises 44 simple multi-instrument classical music pieces assembled from coordinated but separately recorded performances of individual tracks. For each piece, we provide the musical score in MIDI format, the audio recordings of the individual tracks, the audio and video recording of the assembled mixture, and ground-truth annotation files including frame-level and note-level transcriptions. We describe our methodology for the creation of the dataset, particularly highlighting our approaches to address the challenges involved in maintaining synchronization and expressiveness. We demonstrate the high quality of synchronization achieved with our proposed approach by comparing the dataset with existing widely used music audio datasets. We anticipate that the dataset will be useful for the development and evaluation of existing music information retrieval (MIR) tasks, as well as for novel multimodal tasks. We benchmark two existing MIR tasks (multipitch analysis and score-informed source separation) on the dataset and compare them with other existing music audio datasets. In addition, we consider two novel multimodal MIR tasks (visually informed multipitch analysis and polyphonic vibrato analysis) enabled by the dataset and provide evaluation measurements and baseline systems for future comparisons (from our recent work). Finally, we propose several emerging research directions that the dataset enables. We describe a neural network architecture which enables prediction and composition of polyphonic music in a manner that preserves translation-invariance of the dataset. Specifically, we demonstrate training a probabilistic model of polyphonic music using a set of parallel, tied-weight recurrent networks, inspired by the structure of convolutional neural networks. This model is designed to be invariant to transpositions, but otherwise is intentionally given minimal information about the musical domain, and tasked with discovering patterns present in the source dataset. We present two versions of the model, denoted TP-LSTM-NADE and BALSTM, and also give methods for training the network and for generating novel music. This approach attains high performance at a musical prediction task and successfully creates note sequences which possess measure-level musical structure. We present a content-based automatic music tagging algorithm using fully convolutional neural networks (FCNs). We evaluate different architectures consisting of 2D convolutional layers and subsampling layers only. In the experiments, we measure the AUC-ROC scores of the architectures with different complexities and input types using the MagnaTagATune dataset, where a 4-layer architecture shows state-of-the-art performance with mel-spectrogram input. Furthermore, we evaluated the performances of the architectures with varying the number of layers on a larger dataset (Million Song Dataset), and found that deeper models outperformed the 4-layer architecture. The experiments show that mel-spectrogram is an effective time-frequency representation for automatic tagging and that more complex models benefit from more training data. A method is described which analyzes the basic pattern of beats in a piece of music, the musical meter. The analysis is performed jointly at three different time scales: at the temporally atomic tatum pulse level, at the tactus pulse level which corresponds to the tempo of a piece, and at the musical measure level. Acoustic signals from arbitrary musical genres are considered. For the initial time-frequency analysis, a new technique is proposed which measures the degree of musical accent as a function of time at four different frequency ranges. This is followed by a bank of comb filter resonators which extracts features for estimating the periods and phases of the three pulses. The features are processed by a probabilistic model which represents primitive musical knowledge and uses the low-level observations to perform joint estimation of the tatum, tactus, and measure pulses. The model takes into account the temporal dependencies between successive estimates and enables both causal and noncausal analysis. The method is validated using a manually annotated database of 474 music signals from various genres. The method works robustly for different types of music and improves over two state-of-the-art reference methods in simulations. Generating music has a few notable differences from generating images and videos. First, music is an art of time, necessitating a temporal model. Second, music is usually composed of multiple instruments tracks, with close interaction with one another. Each track has its own temporal dynamics, but collectively they unfold over time interdependently. Lastly, for symbolic domain music generation, the targeted output is sequences of discrete musical events, not continuous values. In this paper, we propose and study three generative adversarial networks (GANs) for symbolic-domain multi-track music generation, using a data set of 127,731 MIDI bars of pop rock music. The three models, which differ in the underlying model assumption and accordingly the network architecture, are referred to as the jamming model, composer model, and hybrid model, respectively. We propose a few intra-track and inter-track objective metrics to examine and compare their generation result, in addition to a subjective evaluation. We show that our models can learn from the noisy MIDI files and generate coherent music of four bars right from scratch (i.e. without human inputs). We also propose extensions of our models to facilitate human-AI cooperative music creation: given the piano track composed by human we can generate four additional tracks in return to accompany it. The capacity for real-time synchronization and coordination is a common ability among trained musicians performing a music score that presents an interesting challenge for machine intelligence. Compared to speech recognition, which has influenced many music information retrieval systems, music's temporal dynamics and complexity pose challenging problems to common approximations regarding time modeling of data streams. In this paper, we propose a design for a real-time music-to-score alignment system. Given a live recording of a musician playing a music score, the system is capable of following the musician in real time within the score and decoding the tempo (or pace) of its performance. The proposed design features two coupled audio and tempo agents within a unique probabilistic inference framework that adaptively updates its parameters based on the real-time context. Online decoding is achieved through the collaboration of the coupled agents in a Hidden Hybrid Markov semi-Markov framework, where prediction feedback of one agent affects the behavior of the other. We perform evaluations for both real-time alignment and the proposed temporal model. An implementation of the presented system has been widely used in real concert situations worldwide and the readers are encouraged to access the actual system and experiment the results. We study indeterminacies in realization of ornaments and how they can be incorporated in a stochastic performance model applicable for music information processing such as score-performance matching. We point out the importance of temporal information, and propose a hidden Markov model which describes it explicitly and represents ornaments with several state types. Following a review of the indeterminacies, they are carefully incorporated into the model through its topology and parameters, and the state construction for quite general polyphonic scores is explained in detail. By analysing piano performance data, we find significant overlaps in inter-onset-interval distributions of chordal notes, ornaments, and inter-chord events, and the data is used to determine details of the model. The model is applied for score following and offline score-performance matching, yielding highly accurate matching for performances with many ornaments and relatively frequent errors, repeats, and skips. Identifying musical instruments in polyphonic music recordings is a challenging but important problem in the field of music information retrieval. It enables music search by instrument, helps recognize musical genres, or can make music transcription easier and more accurate. In this paper, we present a convolutional neural network framework for predominant instrument recognition in real-world polyphonic music. We train our network from fixed-length music excerpts with a single-labeled predominant instrument and estimate an arbitrary number of predominant instruments from an audio signal with a variable length. To obtain the audio-excerpt-wise result, we aggregate multiple outputs from sliding windows over the test audio. In doing so, we investigated two different aggregation methods: one takes the class-wise average followed by normalization, and the other perform temporally local class-wise max-pooling on the output probability prior to averaging and normalization steps to minimize the effect of averaging process suppresses the activation of sporadically appearing instruments. In addition, we conducted extensive experiments on several important factors that affect the performance, including analysis window size, identification threshold, and activation functions for neural networks to find the optimal set of parameters. Our analysis on the instrument-wise performance found that the onset type is a critical factor for recall and precision of each instrument. Using a dataset of 10k audio excerpts from 11 instruments for evaluation, we found that convolutional neural networks are more robust than conventional methods that exploit spectral features and source separation with support vector machines. Experimental results showed that the proposed convolutional network architecture obtained an F1 measure of 0.619 for micro and 0.513 for macro, respectively, achieving 23.1 and 18.8 in performance improvement compared with the state-of-the-art algorithm. Music auto-tagging is often handled in a similar manner to image classification by regarding the two-dimensional audio spectrogram as image data. However, music auto-tagging is distinguished from image classification in that the tags are highly diverse and have different levels of abstraction. Considering this issue, we propose a convolutional neural networks (CNN)-based architecture that embraces multi-level and multi-scaled features. The architecture is trained in three steps. First, we conduct supervised feature learning to capture local audio features using a set of CNNs with different input sizes. Second, we extract audio features from each layer of the pretrained convolutional networks separately and aggregate them altogether giving a long audio clip. Finally, we put them into fully connected networks and make final predictions of the tags. Our experiments show that using the combination of multi-level and multi-scale features is highly effective in music auto-tagging and the proposed method outperforms the previous state-of-the-art methods on the MagnaTagATune dataset and the Million Song Dataset. We further show that the proposed architecture is useful in transfer learning. We propose a new approach to instrument recognition in the context of real music orchestrations ranging from solos to quartets. The strength of our approach is that it does not require prior musical source separation. Thanks to a hierarchical clustering algorithm exploiting robust probabilistic distances, we obtain a taxonomy of musical ensembles which is used to efficiently classify possible combinations of instruments played simultaneously. Moreover, a wide set of acoustic features is studied including some new proposals. In particular, signal to mask ratios are found to be useful features for audio classification. This study focuses on a single music genre (i.e., jazz) but combines a variety of instruments among which are percussion and singing voice. Using a varied database of sound excerpts from commercial recordings, we show that the segmentation of music with respect to the instruments played can be achieved with an average accuracy of 53 . In a recent conference paper, we have reported a rhythm transcription method based on a merged-output hidden Markov model HMM that explicitly describes the multiple-voice structure of polyphonic music. This model solves a major problem of conventional methods that could not properly describe the nature of multiple voices as in polyrhythmic scores or in the phenomenon of loose synchrony between voices. In this paper, we present a complete description of the proposed model and develop an inference technique, which is valid for any merged-output HMMs, for which output probabilities depend on past events. We also examine the influence of the architecture and parameters of the method in terms of accuracies of rhythm transcription and voice separation and perform comparative evaluations with six other algorithms. Using MIDI recordings of classical piano pieces, we found that the proposed model outperformed other methods by more than 12 points in the accuracy for polyrhythmic performances and performed almost as good as the best one for non-polyrhythmic performances. This reveals the state-of-the-art methods of rhythm transcription for the first time in the literature. Publicly available source codes are also provided for future comparisons. We propose a system, the Continuator, that bridges the gap between two classes of traditionally incompatible musical systems: (1) interactive musical systems, limited in their ability to generate stylistically consistent material, and (2) music imitation systems, which are fundamentally not interactive. Our purpose is to allow musicians to extend their technical ability with stylistically consistent, automatically learnt material. This goal requires the ability for the system to build operational representations of musical styles in a real time context. Our approach is based on a Markov model of musical styles augmented to account for musical issues such as management of rhythm, beat, harmony, and imprecision. The resulting system is able to learn and generate music in any style, either in standalone mode, as continuations of musician’s input, or as interactive improvisation back up. Lastly, the very design of the system makes possible new modes of musical collaborative playing. We describe the architectu...