Play as You Like: Timbre-enhanced Multi-modal Music Style Transfer

Play as You Like: Timbre-enhanced Multi-modal Music Style Transfer

Chien-Yu Lu,1 Min-Xin Xue,1* Chia-Che Chang,1 Che-Rung Lee,1 Li Su2
1Department of Computer Science, National Tsing-Hua University, Hsinchu, Taiwan
2Institute of Information Science, Academia Sinica, Taipei, Taiwan
{j19550713, liedownisok, chang810249}@gmail.com, cherung@cs.nthu.edu.tw, lisu@iis.sinica.edu.tw
The first two authors are with equal contribution.
Abstract

Style transfer of polyphonic music recordings is a challenging task when considering the modeling of diverse, imaginative, and reasonable music pieces in the style different from their original one. To achieve this, learning stable multi-modal representations for both domain-variant (i.e., style) and domain-invariant (i.e., content) information of music in an unsupervised manner is critical. In this paper, we propose an unsupervised music style transfer method without the need for parallel data. Besides, to characterize the multi-modal distribution of music pieces, we employ the Multi-modal Unsupervised Image-to-Image Translation (MUNIT) framework in the proposed system. This allows one to generate diverse outputs from the learned latent distributions representing contents and styles. Moreover, to better capture the granularity of sound, such as the perceptual dimensions of timbre and the nuance in instrument-specific performance, cognitively plausible features including mel-frequency cepstral coefficients (MFCC), spectral difference, and spectral envelope, are combined with the widely-used mel-spectrogram into a timber-enhanced multi-channel input representation. The Relativistic average Generative Adversarial Networks (RaGAN) is also utilized to achieve fast convergence and high stability. We conduct experiments on bilateral style transfer tasks among three different genres, namely piano solo, guitar solo, and string quartet. Results demonstrate the advantages of the proposed method in music style transfer with improved sound quality and in allowing users to manipulate the output.

Play as You Like: Timbre-enhanced Multi-modal Music Style Transfer


Chien-Yu Lu,1thanks: The first two authors are with equal contribution. Min-Xin Xue,1* Chia-Che Chang,1 Che-Rung Lee,1 Li Su2 1Department of Computer Science, National Tsing-Hua University, Hsinchu, Taiwan 2Institute of Information Science, Academia Sinica, Taipei, Taiwan {j19550713, liedownisok, chang810249}@gmail.com, cherung@cs.nthu.edu.tw, lisu@iis.sinica.edu.tw

Copyright © 2019, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

Introduction

The music style transfer problem has been receiving increasing attention in the past decade (Dai and Xia, 2018). When discussing this problem, typically we assume that music can be decomposed into two of its attributes, namely content and style, the former being domain-invariant and the latter domain-variant. This problem is therefore considered as to modify the style of a music piece while preserving its content. However, the boundary that distinguishing content and style is highly dynamic; different objective functions in timbre, performance style or composition are related to different style transfer problems (Dai and Xia, 2018). Traditional style transfer methods based on feature interpolation (Caetano and Rodet, 2011) or matrix factorization (Driedger, Prätzlich, and Müller, 2015; Su et al., 2017) typically need a parallel dataset containing musical notes in the target-domain style, and every note has a pair in the source domain. In other words, we need to specify the content attribute element-wisely, and make style transfer be performed in a supervised manner. Such restriction highly limits the scope that the system can be applied. To achieve higher-level mapping across domains, recent approaches using deep learning methods such as the generative adversarial networks (GAN) (Goodfellow et al., 2014) allow a system to learn the content and style attributes directly from data in an unsupervised manner with extra flexibility in mining the attributes relevant to content or style (Ulyanov and Lebedev, 2016; Bohan, 2017; Wu et al., 2018; Verma and Smith, 2018; Haque, Guo, and Verma, 2018; Mor et al., 2018).

Beyond the problem of unsupervised domain adaptation, there are still technical barriers concerning realistic music style transfer applicable for various kinds of music. First, previous studies can still hardly achieve multi-modal and non-deterministic mapping between different domains. However, when we transfer a piano solo piece into guitar solo, we often expect the outcome of the guitar solo to be adjustable, perhaps with various fingering styles, brightness, musical texture, or other sound quality. Second, the transferred music inevitably undergoes degradation of perceptual quality such as severely distorted musical timbre; this indicates the need of a better representation for timbre information. Although many acoustic correlates of timbre have been verified via psychoacoustic experiments (Grey, 1977; Alluri and Toiviainen, 2010; Caclin et al., 2005) and also been used in music information retrieval (Lartillot, Toiviainen, and Eerola, 2008; Peeters et al., 2011), they are rarely discussed in deep-learning-based music style transfer problems. This might be because of several reasons: some acoustic correlates are incompatible to the format of modern deep learning architectures; rawer data inputs such as waveforms and spectrograms are still preferred to reveal the strength of deep learning; and even, an exact theory of those acoustic correlates on human perception is still not clear in cognitive science (Siedenburg, Fujinaga, and McAdams, 2016; Aucouturier and Bigand, 2013). For this issue, a recently proposed method in (Mor et al., 2018) adopts the WaveNet (Van Den Oord et al., 2016), the state-of-the-art waveform generator on raw waveform data to generate realistic outputs for various kinds of music with a deterministic style mapping, at the expense of massive computing power.

To address these issues, we consider the music style transfer problem as learning a multi-modal conditional distribution of style in the target domain given only one unpaired sample in the source domain. This is similar to the Multi-modal Unsupervised Image-to-Image Translation (MUNIT) problem, where a principled framework proposed in (Huang et al., 2018) is employed in our system. During training, cognitively plausible timbre features including mel-frequency cepstral coefficients (MFCC), spectral difference, and spectral envelope, all designed to have the same dimension with mel-spectrogram, are combined together into a multi-channel input representation in the timbre space. Since these features have close-form relationship with each other, we introduce a new loss function, named intrinsic consistency loss, to keep the consistency among the channel-wise features in the target domain. Experiments show that with such extra conditioning on the timbre space, the system does achieve better performance in terms of content preservation and sound quality than those using only the spectrogram. Moreover, comparing to other style transfer methods, the proposed multi-modal method can stably generate diverse and realistic outputs withs improved quality. Also, in the learned representations, some dimensions that disentangle timbre can be observed. Our contributions are two-fold:

  • We propose an unsupervised multi-modal music style transfer system for one-to-many generation. To the best of our knowledge, this have not been done before in music style transfer. The proposed system further allows music style transfer from scratch, without massive training data.

  • We design multi-channel timbre features with the proposed intrinsic consistency loss to improve the sound quality for better listening experience of the style-transferred music. Disentanglement of timbre characteristics in the encoded latent space is also observed.

Related Works

Generative Adversarial Networks

Since its invention in (Goodfellow et al., 2014), the GAN has shown amazing results in multimedia content generation in variant domains (Yu et al., 2017; Gwak et al., 2017; Li et al., 2017). A GAN comprises two core components, namely the generator and the discriminator. The task of the generator is to fool the discriminator, which distinguishes real samples from generated sample. This loss function, named adversarial loss, is therefore implicit and is defined only by the data. Such a property is particularly powerful for generation tasks.

Domain Adaptation

Recent years has witnessed considerable success in unsupervised domain adaptation problems without parallel data, such as image colorization (Larsson, Maire, and Shakhnarovich, 2016; Zhang, Isola, and Efros, 2016) and image enhancement (Chen et al., 2018). Two of the most popular methods that achieve unpaired domain adaptation could be the CycleGAN (Zhu et al., 2017a) and the Unsupervised Image-to-Image Translation Networks (UNIT) (Liu, Breuel, and Kautz, 2017) framework, the former introduce the cycle consistency loss to train with unpaired data and the other is to learn a joint distribution of images in different domains. However, most of these transfer models are based on a deterministic or one-to-one mapping. Therefore, these models are unable to generate diverse outputs when given the data from source domain. One of the earliest attempts on multi-modal unsupervised translation could be (Zhu et al., 2017b), which aims at capturing the distribution of all possible outputs, that means, a one-to-many mapping that maps a single input into multiple outputs. To handle multi-modal translation, two possible methods are: adding random noise to the generator, or adding dropout layer into the generator for capturing the distribution of outputs. However, these methods still tend to generate similar outputs since the generator is easy to ignoring random noise and additional dropout layers. In this paper, we use a disentangled representation framework, MUNIT (Huang et al., 2018), for generating high-quality and high-diversity music pieces with unpaired training data.

Music Style Transfer

The music style transfer problem has been investigated for decades. Broadly speaking, the music being transferred can be either audio signals or symbolic scores (Dai and Xia, 2018). In this paper, we focus on the music style transfer of audio signals, where its domain-invariant content typically refer to the structure established by the composer (e.g., mode, pitch, or dissonance)111Although the instrumentation process is usually done by the composer, especially in Western classical music, we presume that the timbre (i.e., the instrument chosen for performance) is determined by the performer., and its domain-variant style refers to the interpretation of the performer (e.g., timbre, playing styles, expression).

With such abundant implications of content and style, the music style transfer problem encompasses extensive application scenarios, including audio mosaicking (Driedger, Prätzlich, and Müller, 2015), audio antiquing (Välimäki et al., 2008; Su et al., 2017), and singing voice conversion (Kobayashi et al., 2014; Wu et al., 2018), to name but a few. Recently, motivated by the success of image style transfer (Gatys, Ecker, and Bethge, 2016), using deep learning for music or speech style transfer on audio signals has caught wide attention. These solutions can be roughly categorized into two classes. The first class takes spectrogram as input and feeds it into convolutional neural networks (CNN), recurrent neural networks (RNN), GAN or autoencoder (Haque, Guo, and Verma, 2018; Donahue, McAuley, and Puckette, 2018). Cycle consistency loss has also been applied for such features (Wu et al., 2018; Hosseini-Asl et al., 2018). The second class takes raw waveform as input and feed it into autoregressive models such as WaveNet (Mor et al., 2018). Unlike the classical approaches, the deep learning approaches pay less attention to the level of signal processing, and tends to overlook timbre-related features that are psychoacoustically meaningful in describing music styles. One notable exception is (Verma and Smith, 2018), which took the deviation of temporal and frequency energy envelopes respectively from the style audio into the loss function of the network, and demonstrated promising results.

Data Representation

We discuss the audio features before introducing the whole framework of the proposed system. We set two criteria of choosing features for our system input. First, all the features can be of the same dimension, so as to facilitate a CNN-based multi-channel architecture, where one feature occupy one input channel. In other words, the channel-wise features represent the colors of sound; this is similar to the case of image processing, where three colors (i.e., R, G, and B) are also taken as channel-wise input. Second, the chosen features should be related to music perception or music signal synthesis. The features verified to be highly correlated to one or more attributes of musical timbre through perceptual experiments are preferred more. As a result, we consider the following four data representations: 1) mel-spectrogram, 2) mel-frequency cepstral coefficients (MFCC), 3) spectral difference, and 4) spectral envelope.

Consider an input signal where is the index of time. Give a -point window function for the computation of the short-time Fourier transform (STFT):

(1)

where is the frequency index. The sampling rate is kHz. We consider the power spectrogram of being the -power of the magnitude part of the STFT, namely . In this paper we set , a value that well approximate the perceptual scale based on the Stevens power law (Stevens, 1957). The mel-spectrogram is the power spectrogram mapped into the mel-frequency scale with a filterbank. The filterbank has 256 overlapped triangular filters ranging from zero to 11.025 kHz, and the filters are equally-spaced in the mel scale: . MFCC is represented as the discrete cosine transform (DCT) of the mel-spectrum:

(2)

where is the cepstral index and is the number of frequency bands. The MFCC has been one of the most widely used audio feature ranging from a wide diversity of tasks including speech recognition, speaker identification, music classification, and many others. Traditionally, only the first few coefficients of the MFCC are used, as these coefficients are found relevant to timbre-related information. High-quefrency coefficients are then related to pitch. In this work, we adopt all coefficients for end-to-end training.

The spectral difference is a classic feature for musical onset detection and timbre classification. It is highly relevant to the attack in the attack-decay-sustain-release (ADSR) envelope of a note. The spectral difference is represented as

(3)

where ReLU refers to a rectified linear unit that discards the energy-decreasing parts in the time-frequency plane. The accumulation of spectral difference over the frequency axis is the well-known spectral flux for musical onset detection.

The spectral envelope can be loosely estimated through the inverse DCT of the first elements of the MFCC, which represents the slow-varying counterpart in the spectrum:

(4)

where is the cutoff cepstral index. In this paper we set . The spectral envelope has been a well-known factor in timbre and is widely used in sound synthesis []. These data representations emphasize different aspects of timbre, and at the same time able to act as a channel for joint learning.

Proposed Method

Consider the style transfer problem from two domains and . and are two samples from and , respectively. Assume that the latent spaces of the two domains are partially shared: each is generated by a content code shared by both domains and a style code in the individual domain. Inferring the marginal distributions of and , namely and , respectively, therefore allows one to achieve one-to-many mapping between and . This idea was first proposed in the MUNIT framework (Huang et al., 2018). To further improve its performance and to adapt to our problem formulation, we make two extensions. First, to stabilize the generation result and speed up the convergence rate, we adopt the Relativistic average GAN (RaGAN) (Jolicoeur-Martineau, 2018) instead of the for the conventional GAN component for generation. Second, considering the relation between the channel-wise timbre features, we introduce the intrinsic consistency loss to pertain the relation between the output features.

Figure 1: The proposed multi-modal music style transfer system with intrinsic consistency regularization . Left: cross-domain architecture. Right: self-reconstruction.

Overview

Fig. 1 conceptually illustrates the whole multi-mdoal music style transfer architecture. It contains encoders and generators for domains and , namely , , , and .222Since the transfer task is bilateral, we will ignore the subscript if we do not specifically mention or domains. For example, refers to either or encodes a music piece into a style code and a content code . decodes and into the transferred result, where and are from different domains and in the target domain is sampled from a Gaussian distribution . For example, the process where transfer in domain to in domain . Similarly, the process transferring in domain to in domain is also shown in Fig. 1.

The system has two main networks, cross-domain translation and within-domain reconstruction, as shown in the left and the right of Fig. 1, respectively. The cross-domain translation network uses GANs to match the distribution of the transferred features to the distribution of the features in the target domain. It means, discriminators should distinguish the transferred samples from the ones truly in the target domain, and needs to fool by capturing the distribution of the target domain.

By adopting the Chi-Square loss (Mao et al., 2017) in the GANs, the resulting adversarial loss, , is represented as:

(5)

where is a marginal distribution from which is sampled. Besides, we expect that the content code of a given sample should remain the same after cross-domain style transfer. This is done by minimizing the content loss ():

(6)

where is the -norm, () is the content code before style transfer, and () is the content code after style transfer. Similarly, we also expect the style code of the transferred result to be the same as the one sampled before style transfer. This is done by minimizing the style loss :

(7)

where and are the transferred style codes, and and are two input style codes sampled from .

Finally, the system also incorporates self-reconstruction mechanism, as shown in the right of Fig. 1. For example, should be able to reconstruct from the latent codes that encodes. The reconstruction loss is

(8)

where and are the reconstructed features of and , respectively.

RaGAN

One of our goals is to translate music pieces into the target domain with improved sound quality. To do this, we adopt the recently-proposed Relativistic average GAN (RaGAN) (Jolicoeur-Martineau, 2018) as our GAN training methodology to generate high quality and stable outputs. RaGAN is different from other GAN architectures in that in the training stage, the generator not only captures the distribution of real data, but also decreases the probability that real data is real. The RaGAN discriminator is designed as

(9)

where is the sigmoid function, is the layer before the sigmoid output layer of the discriminator, and is the input data. is the distribution of real data, is the distribution of fake data. and denote real and fake data, respectively.

Figure 2: Illustration of pre-processing and post processing on audio signals. The power-scale spectrogram and the phase spectrogram are derived from the short-time Fourier transform . To reconstruct the generated mel-spectrogram , the NNLS optimization and the original phase spectrogram are used to get a stable reconstructed signal via the ISTFT.

Intrinsic Consistency Loss

To achieve one-to-many mapping, the MUNIT framework deprecates the cycle consistency loss that is only applicable in one-to-one settings. We needs extra ways to guarantee the robustness of the transferred features. By noticing that the multi-channel features are all derived from the mel-spectrogram with closed forms, we propose a new regularization term to guide the transferred features to be with the same closed-form relation. In other words, the intrinsic relations among the channels should remain the same after style transfer. First, the MFCC channel should remain the DCT of the mel-spectrogram:

(10)

where is the transferred MFCC and is the transferred mel-spectrogram. Similar loss functions can also be designed for spectral difference and spectral envelope:

(11)
(12)

That means, the transferred spectral difference (e.g., ) should remain as the spectral difference of the transferred mel-spectrogram (e.g., ). The case of spectral envelope is also similar. The total intrinsic consistency loss is

(13)

and the full objective function of our model is

(14)

where , and are hyper-parameters to reconstruction loss.

Signal Reconstruction

The style-transferred music signal is reconstructed from the mel-spectrogram and the phase spectrogram of the input signal. This is done in the following steps. First, since the mel-spectrogram is nonnegative, we can convert it back to a linear-frequency spectrogram through the mel-filterbank using the nonnegative least square (NNLS) optimization:

(15)

The resulting magnitude spectrum is therefore . Then, the complex-valued time-frequency representation is processed by the inverse short-time Fourier transform (ISTFT), and the final audio is obtained. The process dealing with waveforms is illustrated in Fig. 2.

Implementation details

The adopted networks are mostly based on the MUNIT implementation except for the RaGAN in adversarial training. The model is optimized by adam, with the batch size being one, and with the learning rate and weight decay rate being both 0.0001. The regularization parameters in (13) and (14) are: , , and . The sampling rate of music signals is kHz. The window size and hop size for STFT are 2048 and 256 samples, respectively. The dimension of the style code is 8.

Experiment and Results

In the experiments, we consider two music style transfer tasks using the following experimental data:

  1. Bilateral style transfer between classical piano solo (Nocturne Complete Works performed by Vladimir Ashkenazy) and classical string quartet (Bruch’s Complete String Quartet).

  2. Bilateral style transfer between popular piano solo and popular guitar solo (data of both domains consists in 34 piano solos (8,200 seconds) and 56 guitar solos (7,800 seconds) covered by the pianists and guitarists on YouTube. Please see supplementary materials for details).

In brief, there are four subtasks in total: piano to guitar (P2G), guitar to piano (G2P), piano to string quartet (P2S), and string quartet to piano (S2P).

For each subtask, we evaluate the proposed system in two stages, the first being the comparison to baseline models and the second the comparison to baseline features. For the two baseline models, we consider CycleGAN (Zhu et al., 2017a) and UNIT (Liu, Breuel, and Kautz, 2017), which are both competitive unsupervised style transfer networks. Note that the two baseline models allow only one-to-one mapping. For the features, we consider using mel-spectrogram only (MS), mel-spectrogram and MFCC (MC), and all four features (ALL). For simplicity, we do not exhaust all possible combinations of these settings. Instead, we consider the following five cases: CycleGAN-MS, UNIT-MS, MUNIT-MS, MUNIT-MC, and MUNIT-ALL. These cases suffice the comparison on both feature and model.

Subjective tests were conducted to evaluate the style transfer system from human’s perspective. For each subtask, one input music clip is transferred using the above five settings. CycleGAN and UNIT both generate one output sample, and for MUNIT-based methods, we randomly select three style codes in the target domain and obtain three output samples. This results in a huge amount of listening samples, so we split the test into six different questionnaires, three of them comparing models and the other three three comparing features. By doing so, only one out of the three MUNIT-based output needs to be selected in a questionnaire. A participant only needs to complete one randomly selected questionnaire to finish one subjective test.

In each round, a subject first listens to the original music clip, then its three style-transferred versions using different models (i.e., CycleGAN, UNIT, MUNIT) or different features (i.e., MS, MC, ALL). For each transferred version, the subject is asked to score three problems from 1 (low) to 5 (high). The three problems are:

Figure 3: Comparison of the input (original) and output (transferred) mel-spectrograms for CycleGAN-MS (the upper two rows), UNIT-MS (the middle two rows), and MUNIT-MS (the lower two rows). The four subtasks demonstrated in every two rows are: P2S (upper left), S2P (upper right), P2G (lower left), and G2P (lower right).
  1. Success in style transfer (ST): how well does the style of the transferred version match the target domain,

  2. Content preservation (CP): how well does the content of the transferred version match the original version, and

  3. Sound quality (SQ): how good is the sound.

After the scoring process, the subject is asked to choose the best and the worst version according to her/his personal view on style transfer. This part is a preference test.

Figure 4: Illustration of the input (original) and output (transferred) feature using MUNIT-ALL of on P2G (the left two columns) and G2P (the right two columns). From top to bottom: mel-spectrogram, MFCC, spectral difference, and spectral envelope.
Task P2G G2P P2S S2P Average
Model Feature ST CP SQ ST CP SQ ST CP SQ ST CP SQ ST CP SQ
CycleGAN MS 2.89 4.27 2.56 2.66 4.17 2.57 2.85 3.51 2.33 3.21 4.01 3.10 2.90 3.99 2.64
UNIT MS 2.85 4.07 2.80 2.57 3.83 2.20 2.83 3.62 2.28 3.39 3.90 2.88 2.91 3.85 2.54
MUNIT MS 2.97 3.98 2.64 3.06 3.91 2.48 2.88 3.45 2.43 3.55 3.56 2.88 3.12 3.72 2.61
MUNIT MC 3.30 4.07 3.14 2.80 3.56 2.42 2.77 3.32 2.27 3.47 3.44 2.92 3.09 3.60 2.69
MUNIT ALL 3.55 4.12 3.13 2.95 4.02 2.97 2.12 3.11 1.93 3.76 3.70 3.25 3.09 3.74 2.82
Table 1: The mean opinion score (MOS) of various style transfer tasks and settings. From top to bottom: CycleGAN-MS, UNIT-MS, MUNIT-MS, MUNIT-MC, MUNIT-ALL. See the supplementary material for details about the details of evaluation.
Figure 5: Results of the preference test. Left: comparison of models. Right: comparison of features. The y-axis is the ratio that each setting earns the best, middle, or the worst ranking from the listeners.
Figure 6: Converted mel-spectrograms from a piano music clip in the P2G task with the 6th dimension of the sampled style code varying from -3 to 3. The horizontal axis refers to time. Audio samples are available in the supplementary material.

Subjective Evaluation

Table 1 shows the Mean Opinion Scores (MOS) of the listening test collected from 182 responses. First, by comparing the three models, we can see that CycleGAN performs best in content preservation after domain transfer, possibly because of the strength of the cycle consistency loss in matching the target domain directly at the feature level.

On the other hand, MUNIT outperforms the other two models in terms of style transfer and sound quality. Second, by comparing the features, we can see that using ALL features outperforms others by 0.1 in the average sound quality score. For content preservation and style transfer, however, the number of feature is rather insensitive. While MUNIT-based methods get the highest scores in style transfer, which shows learning a multi-modal conditional distribution better generates realistic style-transfered output, we can’t see the relation between multi-channel features and style transfer quality. However, the sound quality evaluation shows that MUNIT-ALL conducts the best sound quality.

The above results indicate an unsurprising trade-off between style transfer and content preservation. The overall evaluation of listeners’ preference on those music style transfer systems could be better seen from the preference test result. The results are shown in Fig. 5. For the comparison of models, up to 48% of listeners view MUNIT-MS as the best, and only 24% of listeners views it as the worst. On the other side, CycleGAN-MS gets the most “worst” votes and MNUIT-MS gets the least. For the comparison of features, 43% of the listeners view MUNIT-ALL as the best, and at the same time 42% of the listeners view MUNIT-MS as the worst. These results demonstrate the superiority of the proposed method over other baselines.

Illustration of Examples

Fig. 3 compares the input and output mel-spectrograms among different models and tasks. From the illustrations one may observe that all the models generate some characteristics related to the target domain. For example, we observe that in the P2S task, there are vibrato notes in the output, and in the P2G task, the high-frequency components are suppressed. More detailed feature characteristics can be seen in Fig. 4 where all the four features in an P2G task are shown. For the output in guitar solo style, one may further observe longer note attacks shown in the spectral difference, and less high-frequency parts in spectral envelope, both of which are indeed characteristics of guitar.

Style Code Interpolation

We then investigate how a specific dimension of the style code can affect the generation result. Fig. 6 shows a series of P2G examples with interpolated style codes. For a selected style code , we linearly interpolate the 6th dimension of , , with a value from -3 to 3, and generate a series of music pieces based on these modified style code. Interestingly, results show that when increases, the high-frequency parts decreases. In this case, can be related to some timbre features such as spectral centroid or brightness. This phenomena indicates that some of the style code elements do disentangle the characteristics of timbre.

Conclusion

We have presented a novel method to transfer a music pieces into multiple pieces in another style. We have shown that the multi-channel features in the timbre space and the regularization of the intrinsic consistency loss among them improve the sound quality of the transferred music pieces. The multi-modal framework also match the target domain distribution better than previous approaches. In comparison to other style transfer methods, our proposed method is one-to-many, stable, and without the need of paired data and pre-trained model. The learned representation of style is also adjustable. These findings suggest further studies on disentangling timbre characteristics, utilizing the findings from psychoacoustics on the perceptual dimension of music styles, and the speeding up of the music style transfer system. Codes and listening examples of this work are announced online at: https://github.com/ChienYuLu/Play-As-You-Like-Timbre-Enhanced-Multi-modal-Music-Style-Transfer

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Appendices

Experiment Data and Listening Examples

The data of piano solo and guitar solo for training the style transfer models are collected from the web. For reproducibility, we put the YouTube link of the data we used in the experiments into two playlists. The links of the playlists are as follows:

Besides, the listening examples of the generated style-transferred audio in the four subtasks (i.e., P2G, G2P, P2S, and S2P), along with their original version, are available online at https://goo.gl/BhHzec and the GitHub repository: https://github.com/ChienYuLu/Play-As-You-Like-Timbre-Enhanced-Multi-modal-Music-Style-Transfer

Further Details on Subjective Evaluation

In the following we report further details on the subjective evaluation. Our subjective evaluation process is completed through online questionnaires. 182 people joined our subject test. 23 of them are under 20 years old, 127 of them are between 20 and 29, 21 of them are between 30 and 39, and the rest 11 ones are above 40. We did not collect the participants’ gender information, but their background of music training: 58 of the participants reported themselves as professional musicians. We take the responses from these 58 subjects as the responses from musicians, and other responses as from non-musicians.

As mentioned in the paper, we conducted two sets of experiments, one considering the comparison on models and the other on features. The former compares Cycle-MS, UNIT-MS, and MUNIT-MS, while the latter compares MUNIT-MS, MUNIT-MC, and MUNIT-ALL. That means, the setting MUNIT-MS is evaluated in both experiments. What we reported in the paper is the average result of MUNIT-MS. Though merging the two MUNIT-MS results or not do not affect our conclusion of this paper, we can still see more details when reporting them separately. It is valuable for further discussion.

Based on the above reasons, in the supplementary material we further report 1) the mean opinion scores (MOS) given separately from musicians and non-musicians and 2) the two separated MUNIT-MS results in different scenarios of comparison, as listed in Table 2. Table 2 indicates that, first, musicians tend to rate lower scores than non-musicians do in answering the questions in the subjective tests. Second, for most of the questions, the best settings the musicians and non-musicians selected are consistent. For example, in the P2G subtask, we may see from the P2G columns that both musicians and non-musicians evaluate the MUNIT model to outperform others in ST and SQ, and the CycleGAN is the best in CP. Similar observation can also be found in G2P and P2S subtasks.

Second, the two MUNIT-MS results are different. More specifically, the MOS in feature comparison is lower than in the other, since MUNIT-MS is ‘relatively’ inferior to the other two features, and relatively superior to the other two models. This implies the users’ bias when comparing one setting under different scenario.

Finally, there are a subtle disagreement between between musicians and non-musicians when comparing different features: on average, musicians tend to say MC is better than ALL in ST. This is mainly affected by the fact that musicians is much more sensitive than non-musicians to the low quality of the P2S results.

Task P2G G2P P2S S2P Average
 Model Feat BG ST CP SQ ST CP SQ ST CP SQ ST CP SQ ST CP SQ
 CycleGAN MS Y 2.68 4.06 2.52 2.58 3.84 2.68 2.94 3.29 2.10 3.19 4.00 3.19 2.85 3.80 2.62
N 3.02 4.39 2.61 2.71 4.32 2.53 2.85 3.63 2.47 3.20 3.98 3.03 2.94 4.08 2.66
 UNIT MS Y 2.65 3.77 2.68 2.42 3.42 2.23 3.00 3.39 2.39 3.55 3.97 3.03 2.90 3.64 2.58
N 2.95 4.22 2.86 2.64 4.02 2.19 2.76 3.75 2.24 3.32 3.86 2.81 2.92 3.96 2.53
 MUNIT MS Y 3.03 3.81 2.77 3.26 3.81 2.74 3.13 3.45 2.55 3.74 3.74 3.00 3.29 3.70 2.77
N 3.14 4.22 2.86 3.34 4.24 2.68 3.17 3.83 2.64 3.69 3.88 2.98 3.33 4.04 2.79
Task P2G G2P P2S S2P Average
Model Feat BG ST CP SQ ST CP SQ ST CP SQ ST CP SQ ST CP SQ
MUNIT MS Y 2.48 3.89 2.22 2.70 3.81 1.93 2.37 3.26 1.96 3.56 3.37 2.70 2.78 3.58 2.20
N 3.00 3.88 2.55 2.86 3.69 2.40 2.72 3.18 2.37 3.34 3.26 2.80 2.98 3.50 2.53
MUNIT MC Y 3.07 3.96 3.15 2.70 3.63 2.19 2.48 3.48 2.07 3.44 3.52 2.74 2.93 3.65 2.54
N 3.42 4.12 3.14 2.86 3.54 2.52 2.88 3.28 2.38 3.49 3.43 3.02 3.16 3.59 2.77
MUNIT ALL Y 3.37 4.19 2.93 2.41 4.04 2.59 1.59 3.15 1.48 3.78 3.81 3.19 2.79 3.80 2.55
N 3.65 4.11 3.22 3.15 4.03 3.12 2.34 3.12 2.12 3.75 3.68 3.29 3.22 3.73 2.94
Table 2: The mean opinion score (MOS) of various style transfer tasks, models, features (Feat), and with consideration of subjects’ background (BG). The “Y/N” on the third column represents whether the subjects report themselves as professional musicians. The upper part of the Table lists the responses of model comparisons, where we have 31 musicians and 59 non-musicians. On the other hand, the lower part collects the responses of feature comparisons, where we have 27 musicians and 65 non-musicians. Therefore, we have two sets of resulting scores for the setting MUNIT-MS. The highest scores from two are in bold font, as we can see, the best settings the musicians and non-musicians selected are consistent for most of the questions.
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