arXiv subjects

Yun-Ning Hung

Publications and source records attributed to Yun-Ning Hung.

23 records · Page 2Linked to original sources

arXiv2019

Musical Composition Style Transfer via Disentangled Timbre Representations

Music creation involves not only composing the different parts (e.g., melody, chords) of a musical work but also arranging/selecting the instruments to play the different parts. While the former has received increasing attention, the latter has not been much investigated. This paper presents, to the best of our knowledge, the first deep learning models for rearranging music of arbitrary genres. Specifically, we build encoders and decoders that take a piece of polyphonic musical audio as input and predict as output its musical score. We investigate disentanglement techniques such as adversarial training to separate latent factors that are related to the musical content (pitch) of different parts of the piece, and that are related to the instrumentation (timbre) of the parts per short-time segment. By disentangling pitch and timbre, our models have an idea of how each piece was composed and arranged. Moreover, the models can realize "composition style transfer" by rearranging a musical piece without much affecting its pitch content. We validate the effectiveness of the models by experiments on instrument activity detection and composition style transfer. To facilitate follow-up research, we open source our code at https://github.com/biboamy/instrument-disentangle.

eess.AS↗

arXiv2019

Multitask learning for frame-level instrument recognition

For many music analysis problems, we need to know the presence of instruments for each time frame in a multi-instrument musical piece. However, such a frame-level instrument recognition task remains difficult, mainly due to the lack of labeled datasets. To address this issue, we present in this paper a large-scale dataset that contains synthetic polyphonic music with frame-level pitch and instrument labels. Moreover, we propose a simple yet novel network architecture to jointly predict the pitch and instrument for each frame. With this multitask learning method, the pitch information can be leveraged to predict the instruments, and also the other way around. And, by using the so-called pianoroll representation of music as the main target output of the model, our model also predicts the instruments that play each individual note event. We validate the effectiveness of the proposed method for framelevel instrument recognition by comparing it with its singletask ablated versions and three state-of-the-art methods. We also demonstrate the result of the proposed method for multipitch streaming with real-world music. For reproducibility, we will share the code to crawl the data and to implement the proposed model at: https://github.com/biboamy/ instrument-streaming.

cs.SD↗

arXiv2018

Learning Disentangled Representations for Timber and Pitch in Music Audio

Timbre and pitch are the two main perceptual properties of musical sounds. Depending on the target applications, we sometimes prefer to focus on one of them, while reducing the effect of the other. Researchers have managed to hand-craft such timbre-invariant or pitch-invariant features using domain knowledge and signal processing techniques, but it remains difficult to disentangle them in the resulting feature representations. Drawing upon state-of-the-art techniques in representation learning, we propose in this paper two deep convolutional neural network models for learning disentangled representation of musical timbre and pitch. Both models use encoders/decoders and adversarial training to learn music representations, but the second model additionally uses skip connections to deal with the pitch information. As music is an art of time, the two models are supervised by frame-level instrument and pitch labels using a new dataset collected from MuseScore. We compare the result of the two disentangling models with a new evaluation protocol called "timbre crossover", which leads to interesting applications in audio-domain music editing. Via various objective evaluations, we show that the second model can better change the instrumentation of a multi-instrument music piece without much affecting the pitch structure. By disentangling timbre and pitch, we envision that the model can contribute to generating more realistic music audio as well.

cs.SD↗

arXiv2018

Frame-level Instrument Recognition by Timbre and Pitch

Instrument recognition is a fundamental task in music information retrieval, yet little has been done to predict the presence of instruments in multi-instrument music for each time frame. This task is important for not only automatic transcription but also many retrieval problems. In this paper, we use the newly released MusicNet dataset to study this front, by building and evaluating a convolutional neural network for making frame-level instrument prediction. We consider it as a multi-label classification problem for each frame and use frame-level annotations as the supervisory signal in training the network. Moreover, we experiment with different ways to incorporate pitch information to our model, with the premise that doing so informs the model the notes that are active per frame, and also encourages the model to learn relative rates of energy buildup in the harmonic partials of different instruments. Experiments show salient performance improvement over baseline methods. We also report an analysis probing how pitch information helps the instrument prediction task. Code and experiment details can be found at \url{https://biboamy.github.io/instrument-recognition/}.

cs.SD↗

arXiv2017

Hit Song Prediction for Pop Music by Siamese CNN with Ranking Loss

A model for hit song prediction can be used in the pop music industry to identify emerging trends and potential artists or songs before they are marketed to the public. While most previous work formulates hit song prediction as a regression or classification problem, we present in this paper a convolutional neural network (CNN) model that treats it as a ranking problem. Specifically, we use a commercial dataset with daily play-counts to train a multi-objective Siamese CNN model with Euclidean loss and pairwise ranking loss to learn from audio the relative ranking relations among songs. Besides, we devise a number of pair sampling methods according to some empirical observation of the data. Our experiment shows that the proposed model with a sampling method called A/B sampling leads to much higher accuracy in hit song prediction than the baseline regression model. Moreover, we can further improve the accuracy by using a neural attention mechanism to extract the highlights of songs and by using a separate CNN model to offer high-level features of songs.

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