GANs for Musical Style Transfer and Learning

 

Syed Fahar Ali ¹, Dr. Keerti Rai ² , Dr. Swapnil M Parikh ³, Abhinav Rathour ⁴, Manivannan Karunakaran ⁵, Nishant Kulkarni ⁶

 

¹ Associate Professor, School of Journalism, and Mass Communication, Noida International, University, 203201, India

² Associate Professor, Department of Electrical and Electronics Engineering, Arka Jain University, Jamshedpur, Jharkhand, India

³ Professor, Department of Computer science and Engineering, Faculty of Engineering and Technology, Parul institute of Technology, Parul University, Vadodara, Gujarat, India

⁴ Centre of Research Impact and Outcome, Chitkara University, Rajpura- 140417, Punjab, India

⁵ Professor and Head, Department of Information Science and Engineering, JAIN (Deemed-to-be University), Bengaluru, Karnataka, India

⁶ Department of Mechanical Engineering Vishwakarma Institute of Technology, Pune, Maharashtra, 411037, India

 

1. INTRODUCTION

Music as an art of all humankind, has encompassed complicated emotive, cultural, and structural patterns which have appealed to the curiosity of the scientist, artist, and technologist. Due to the introduction of artificial intelligence (AI) and deep learning, automatic creation and transformation of musical content have become an active research topic. Generative Adversarial Networks (GANs) are one of the many possible computational methods that have proven to have exceptional abilities to create, translate, and stylize audio information. GANs, firstly, were suggested by Goodfellow et al. (2014) and comprise a discriminator and a generator that act in opposite directions to create realistic results, which resemble the distribution of natural data. Their achievements in the visual task of image translation, super-resolution, and style transfer prompted researchers to investigate similar tasks in the auditory and musical fields. The term musical style transfer is used to describe the act of moving a musical composition of one style into another, e.g. turning a classical piano composition into a jazz improvisation or an electronic copy of an acoustic one and so forth, without losing the underlying melodic and rhythmic content. Earlier studies also attempted to use traditional methods that were either rule-based systems, Hidden Markov Models (HMMs), or Recurrent Neural Networks (RNNs), but frequently either lost harmonic consistency or rhythmic naturalness Chen et al. (2024). Variational Autoencoders (VAEs) enhanced those with learning the latent representations of musical sequences, but did not generate high-quality and stylistically accurate audio. Instead, GANs have the ability to learn non-linear mappings and refine them adversarially to have even greater perceptual realism and stylistic consistency.

Recent studies have seen the adjustment of CycleGANs and Conditional GANs (cGANs) to music style transfer especially in tasks where paired samples are not available. CycleGANs, which provide a cycle-consistency between input and output space, make it possible to learn unpairedly the style to swap, say, pop and classical music. The use of conditional GANs, in its turn, adds overt conditioning variables, like tempo, key, or instrument type, to the generation process Yuan et al. (2022). Such architectures have been effectively generalized to time-series and spectrogram representations, and have been used to cross-domain learn using feature-based transformations, as opposed to note-level symbolic modeling. The significant difficulty of using GANs to music is the representation and processing of audio signals. As a contrast to images, music has highly temporal dependencies and hierarchies; it has melody, harmony, rhythm and timbre, all developing with time. To overcome this, researchers employ intermediate representations like Mel-frequency cepstral coefficients (MFCCs) and short-time fourier transforms (STFTs) and Mel-spectrograms that represent the spectral and perceptual elements of audio Song et al. (2024). The goal function of the discriminator is then to adjust to these representations such that it is sensitive to musical texture and not the amplitude of the waveform. Figure 1 demonstrates the architecture of GAN that allows learning cross-genre music, transforming it, and synthesizing it. Moreover, adversarial training is still a challenge to balance; mode collapse or musical monotony may take place due to unstable convergence.

 Figure 1

Figure 1 GAN Framework for Cross-Genre Musical Learning and Synthesis

 

In addition to the creative generation, the GAN-based systems of style transfer are transforming music education and the interactive learning. The students are able to imagine and experiment with the stylistic changes and learn more about the attributes of the genres and methods of the composition. In the sound design and composition, GANs help musicians to discover new tonal horizons, automatize the way to mimic styles, and enhance creativity. Live performance augmentation and immersive sound experiences are also potential opportunities of the possibility to do real-time style adaptation.

 

2. Literature Review

2.1. Overview of deep learning in music generation

Deep learning has transformed music generation whereby models are able to learn hierarchial patterns and temporal dependencies directly using raw or symbolic musical data. Feedforward Networks and Recurrent Neural Networks (RNNs) had been used early on in neural melodies prediction and chord progressions modeling. When the architectures of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are introduced, researchers managed to make great progress in terms of capturing long-term musical structures, such as motifs, rhythm, and harmonic transitions. These models were taught to produce sequences of notes or MIDI events that follow style preference of a particular genre or composer Chen et al. (2023). Convolutional Neural Networks (CNNs) also increased the applications of deep learning in music through the analytical capabilities of spectrograms and audio to create timbre synthesis, instrument recognition, or texture generation. Subsequently, natural language processing-inspired attention mechanisms and models known as Transformers allowed learning musical contexts (in parallel) with the goal of enhancing the expressiveness and global consistency of the music generated Yang et al. (2024). Also, polyphonic music generation, audio accompaniment prediction and improvisation have been implemented using hybrid systems that mix CNNs and RNNs.

 

2.2. Previous Models for Style Transfer

Prior to the emergence of GANs, Copyleft Transfers between musical styles were mostly based on Autoencoders, Recurrent Neural Networks (RNNs), and Variational Autoencoders (VAEs). The contribution of autoencoders was offering an unsupervised learning of features, they encode musical input in a compressed latent space and are able to reassemble it with stylus alterations. They were, however, frequently without generative diversity and fine control over the style change Hazra and Byun (2020). The use of rhythmic and melodic dependencies became popular, especially with RNN-based models including LSTMs which have the advantage of dealing with sequences in a sequential manner and are therefore capable of style imitation. Although they were effective at learning temporal patterns, they could not have global coherence particularly in long-compositions or across genres. VAEs proposed probabilistic latent representations, which enabled the task of interpolating between musical styles to be much easier and latent-space arithmetic to be applied to mixing styles. They enabled a higher level of flexibility in the representation of various genres, but the resulting outputs were not as high in frequency content and natural expression Huang and Deng (2023), Lan et al. (2024). Also, scholars fused VAEs and attention mechanisms and convolutional layers to improve the quality of reconstruction. However, such models had fundamental issues when producing perceptually realistic audio because adversarial training was not applied.

 

2.3. Key Advancements in GAN Architectures for Audio Synthesis

The development of Generative Adversarial Networks (GANs) in the synthesis of audio has provided a paradigm shift in the computational synthesis of music. In contrast to previous deterministic frameworks, GANs train the model based on an adversarial training scheme in which the generator is trained to produce real data whereas the discriminator is trained to differentiate real and synthetic data. The release of WaveGAN and SpecGAN marked the first attempts at using GANs with raw waveforms and spectrogram to generate audio, with other elements of the semblance and continuity of a timbre along with rhythm. These models showed that adversarial models were capable of synthesizing subtle acoustic characteristics like instrument resonance and micro-temporal diverse sound changes. Following GANs, Conditioning GANs (cGANs) and CycleGANs enabled more control and versatility in the task of music style transfer, with the implementation of conditioning on such attributes as pitch, tempo, or instrument class, and thus generating music of a specific genre. CycleGANs, which typically operate on the task of image-to-image translation, have been applied to unpaired domain translation within the music domain e.g. classical-to-jazz or piano-to-guitar translation by applying cycle-consistency in order to preserve musical semantics Wen et al. (2020). All these progressive GANs and StyleGAN-style models increased synthesis fidelity by additional hierarchical learning of features as well as latent style modulation.

Table 1

Table 1 Summary on Deep Learning Approaches for Musical Style Transfer and Audio Synthesis
Model	Dataset / Input Type	Feature Representation	Objective / Task
GAN (Original)	Synthetic datasets	Latent noise vectors	Adversarial training concept
RNN-LSTM	MIDI datasets	Pitch, velocity, duration	Music generation
VAE (MusicVAE)	MAESTRO, Lakh MIDI	Latent embedding	Style interpolation & composition
WaveNet Autoencoder	NSynth Dataset	Waveform encoding	Timbre synthesis
CNN + RNN Hybrid	AudioSpectra	STFT, MFCC	Polyphonic music generation
CycleGAN	GTZAN, MUSDB18	Mel-spectrogram	Unpaired music style transfer
Conditional GAN	MagnaTagATune	Chroma, tempo, key	Genre-conditioned generation
SpecGAN	NSynth, UrbanSound8K	Log-magnitude spectrograms	Raw audio synthesis
GANSynth	NSynth	Phase-aligned waveform	Pitch & timbre generation
StyleGAN Audio	GTZAN	Mel-spectrogram	Audio texture transfer
CycleGAN-VC2	Voice Conversion Corpus	Spectral envelopes	Cross-domain voice style transfer
Diffusion-GAN Hybrid	MAESTRO	Harmonic spectral maps	High-fidelity music synthesis
Transformer-GAN	Lakh & JazzNet	Symbolic note embeddings	Long-term composition transfer
Conditional + CycleGAN	Mixed (GTZAN, NSynth, MAESTRO)	MFCC, Mel-spectrogram, Chroma	Cross-genre musical style transfer

 

3. Theoretical Framework

3.1. Structure and working principle of GANs

Generative Adversarial Networks (GANs) are based on the general principle of adversarial learning when two neural networks, the generator (G) and the discriminator (D) are trained oppositely. The generator tries to generate the data that are similar to the real ones and the task of the discriminator is to differentiate between the real and fake data. This structure can be used in music to induce GANs to learn complicated distributions of audio signals or symbolic representations including MIDI events Annaki et al. (2024). The GANs do not explicitly model the probability densities but are capable of producing a variety of samples and contextually valid ones, unlike deterministic models. The balance state of this game is reached when the output of the generator cannot be differentiated by real data, so that GANs can generate high-fidelity, stylistically diverse musical generations based on the rhythmic, harmonic and timbral properties of the target domain Semenoglou et al. (2023).

 

3.2. Adaptation of GANs for Time-Series and Waveform Data

Although GANs were originally used in the context of synthezing static images, they can also be extended to work with time-series and waveforms, which makes them useful in audio and music processing. In contrast to images, audio signal signals have severe temporal dependencies and sequential coherence, and special architectures are needed to process one-dimensional or two-dimensional sequential features Li et al. (2021). WaveGAN and SpecGAN models, made alterations to regular convolutional blocks to take raw waveforms and spectrograms, respectively. Instead of the traditional two-dimensional convolution, the models use one-dimensional transposed convolutions in order to capture local temporal details and maintain phase and amplitude continuity (local temporal features). Time-series adaptation typically consists of the conversion of waveforms to spectral representations (e.g. Mel-spectrograms, MFCCs or constant-Q transforms (CQT)). These attributes are fed into the generator and discriminator, and allow learning in the frequency domain. Temporal convolutions and recurrent layers (LSTM or GRU) are also added to represent long-term rhythmic structures Rakhmatulin et al. (2024). CycleGANs and conditional GANs are extensions of this framework, either introducing conditioning signals (i.e. tempo, pitch or instrument type) or requirements of cyclic consistency between domains which is needed when task performance (unpaired style transfer) is being evaluated unconditioned. Figure 2 presents GAN adaptations that are specialized in their time-series processing and complex waveform data processing. In addition, the methods of stabilization, i.e., Wasserstein loss, gradient penalty, and spectral normalization, help to address the problem of mode collapse and instability common in sequential data.

 Figure 2

Figure 2 Illustrating the Adaptation of GANs for Time-Series and Waveform Data

 

Through these adaptations, GANs become capable of synthesizing time-dependent signals with coherent dynamics, tonal structure, and stylistic fidelity, enabling realistic and adaptive musical outputs across multiple genres and audio modalities.

 

3.3. Role of Generator, Discriminator, and Loss Functions in Musical Context

Within the framework of musical style transfer, all the elements of a GAN have a specific but mutually supportive role. The generator is an open-ended agent, that is, it learns to condition latent noise vectors or input features into synthetic musical outputs that are tonally, rhythmically, and expressively correlated with the target style. It tries to get the structural information of music melody, harmony, timbre, and phrasing with the help of convolutational, recurrent, or attention-based structures. The discriminator on the other hand is a critic and it compares generated samples with actual music data. It offers a comment on perceptual quality, coherence, and stylistic consistency and this directs the generator to get better in the next iteration Mienye et al. (2024). The determinants of loss are the key to optimizing this adversarial play. The conventional binary cross-entropy loss motivates the discriminator to distinguish between the real and the generated data correctly, whereas the generator aims at reducing this classification. Nevertheless, in the case of musical data, the Wasserstein loss with gradient penalty (WGAN-GP) or the least squares loss (LSGAN) is commonly applied to have a stable convergence point and a gradual gradient, which improves the realism of audio. Cycle-consistency loss and content preservation loss are as well included in style transfer to retain the original musical semantics and distort stylistic elements.

 

4. Methodology

4.1. Feature extraction and normalization

The initial step of the musical style transfer process based on GAN is feature extraction and normalization, which makes sure that unprocessed information about music is turned into a meaningful and machine-readable form. In contrast to image data, in music, multidimensional and temporal and spectral data should be encoded in an appropriate way to include features of rhythm, pitch, timbre, and dynamics. Representations that are commonly employed are Mel-frequency cepstral coefficients (MFCCs), Mel-spectrograms, chroma features, spectral contrast, and zero-crossing rate. Such characteristics are based on short-time Fourier transform (STFT) analysis that allows to extract both frequency and time changes that are important in genre and style classification. In the case of symbolic music data like MIDI files, further information is extracted as note pitch sequences, velocity, duration, tempo patterns among others. All of these characteristics are the compositional structure and style of the musical piece as a whole. Upon extraction of features, the normalization methods are then used to normalize the data scale and eliminate bias in the training of the model as well as stability of the numbers. Such techniques as min-max scaling, z-score normalization and even log compression are used to balance the amplitude variations and spectral intensity differences.

 

4.2. GAN architecture design

1)    Conditional GAN

Conditional Generative Adversarial Networks (cGANs) are based on the standard framework of GAN but adds conditional variables that are used to influence the process of data generation. These conditions, expressed in musical terms, like genre, type of instrument, tempo or mood can be synthesized more under control and in context sensitive form. The generator G(z|y) is a model that generates a sample that is conditioned on the random noise z and the attribute y, and the discriminator D(x|y) is a model that determines whether the generated sample has satisfied the condition.

The optimization objective of a cGAN is expressed as:

Conditional GAN Objective Function

 

 

Reconstruction-based Auxiliary Loss

 

 

Within the musical domain, cGANs provide a level of fine control in the style conditioning process, and such a system can be trained to synthesize a particular set of rhythm patterns or tonal hues. Through conditioning, cGANs produce more predictable, expressive and style consistent results than traditional GANs.

2)    CycleGAN

Cycle-Consistent Generative Adversarial Networks (CycleGANs) are unpaired domain translationers and hence they are especially useful in musical style transfer cases where no parallel datasets exist. Two generators, G:X Y and F Y X, are trained to learn to map between source and target musical spaces in this framework, and two discriminators.

CycleGAN Adversarial Loss (Forward Mapping)

 

Cycle-Consistency Loss

 

This is so that when an audio sample is translated to a new style and then translated back to it will not lose its original melodic and rhythmic character. The cycleGANs are therefore capable of bidirectional style transfer that allows domain adaptation across styles (e.g classical vs jazz) and at the same time preserving musical coherence and perceptual integrity.

 

5. Experimental Setup

5.1. Hardware and software specifications

GAN-based musical style transfer can be experimented with a high-performance computing environment that allows working with large-scale audio data and can extensively train a neural and perform its tasks. The computer setup in this research will include an NVIDIA RTX 4090 (24 GB VRAM) graphics card, an Intel Core i9-13900K central processing unit, 64 GB of DDR5 memory and 2 TB of NVMe solid state drive to stream and cache data efficiently. The high-resolution Mel-spectrogram or long waveform sequence have resulted in the deep convolutional and recurrent layers being fundamental in the method of the systems to be trained by the use of deep learning, providing a key benefit in terms of speed and efficiency and the capability to utilize massive quantities of data. The operating system is a 64-bit Ubuntu 22.04 LTS that has provided the software setup with stability and compatibility with CUDA drivers. The experiments are based on Python 3.10 as the main programming language, CUDA 12.1 and cuDNN 8.9 to run optimally on the computer processing unit. The audio process including preprocessing and feature extraction are achieved with the help of Librosa, NumPy, and SciPy, whereas the main deep learning architecture is implemented with PyTorch 2.1.

 

5.2. Implementation Environment and Libraries Used

The GAN framework suggested was implemented in a strong Python-based ecosystem which was optimized to deep learning and audio signal processing. The PyTorch 2.1 library has been chosen to build a model, train it, and perform calculations with the help of GPUs because it is flexible with respect to access to dynamic graphs. The extraction of features, transformation of waveforms, and calculation of Mel-spectrograms were all done with TorchAudio and Librosa, which have fundamental pre-processing capabilities such as resampling, STFT, and normalization. To process data and enhance it, Pandas and NumPy helped to perform efficient operations with arrays and organize data in a form of a set. Scaling, normalization and partitioning of data was done using the Scikit-learn toolkit. The visualization tools were combined (TensorBoard, Seaborn, and Matplotlib), to monitor the adversarial loss curves, the balance between the generators and discriminators, and the development of the spectrograms per epoch. CUDA and cuDNN backends were set up to enable parallelized operations of the gpus to speed up computation. Weights & Biases (wandb) was added to keep track of the experiment and optimize the hyperparameters. The development environment was configured in Anaconda where the virtual environments were separated to control dependencies.

 

6. Applications and Future Scope

6.1. Use in AI-assisted composition and sound design

GAN based systems are reinventing AI-assisted music creation and sound design by offering intelligent systems capable of creating stylistically rich and emotionally expressive music. With adversarial learning, generators are able to generate realistic timbers, melodic sequences, and harmonic progressions that can be used to mimic certain genres or composers. This allows musicians to collaborate with AI, where their creative partners are GANs, which propose changes, chords, rhymes, etc. To sound designers, GANs have the ability to create new sounds and tones using spectral properties of different instruments or environments to create some new hybrid sound. Furthermore, conditional GANs (cGANs) allow composers to control generation according to the parameters desired like tempo, mood, or instrumentation to control and fine-tune generation, increasing control and artistic accuracy. The created system endorses a symbiotic product development- creative artists outline conceptual will, whereas GANs produce realizations of stylistic expression. This kind of integration encourages generative creativity, shortening the production time, and widening the musical exploration to the outside limits.

 

6.2. Integration with Interactive Music Learning Platforms

The implementation of GANs in interactive music learning applications is a paradigm shift in the pedagogical perspective of AI as a mentor and a creative assistant. Style transfer and adaptive generation enable the visual and auditory perception of how one piece of composition is changed through various stylistic genres like classical, jazz, rock, or electronic, as well as helping to improve style literacy and auditory awareness. GANs create musical examples with contextual relevance based on the skills level and style interest of the learner by learning on large collections of the human composition. Practically speaking, this type of systems may provide real-time feedback, propose harmonic corrections or even hypothesize stylistic embellishments in line with the input of the student. This forms a feedback learning process, as the AI dynamically increases or decreases complexity, and offers specific training.

 

7. Results and Analysis

The GAN-based framework proposed showed the best result in the musical style transfer across various musical genres with high perceptual fidelity and style consistency. Fréchet Audio Distance (FAD), Spectral Convergence (SC), and Mean Opinion Score (MOS) quantitative evaluation showed improvement relative to baseline models in VAEs and RNNs. CycleGAN showed a high domain adaptation on unpaired datasets with melodic and rhythmic integrity with a mean MOS of 4.6/5. Conditional GANs provided a fine-adjusted control of such attributes as tempo and timbre.

Table 2

Table 2 Quantitative Evaluation of GAN Models for Musical Style Transfer
Evaluation Metric	Autoencoder	VAE	RNN-LSTM	Conditional GAN
Fréchet Audio Distance (↓)	4.82	3.91	3.64	2.41
Spectral Convergence (↓)	0.342	0.298	0.271	0.191
Mean Opinion Score (MOS, 1–5) (↑)	3.42	3.78	4.01	4.47
Genre Consistency (%) (↑)	78.3	84.7	87.2	92.8
Timbre Fidelity Index (↑)	80.5	82.3	86.4	91.7

 

A comparative analysis of different deep learning models on musical style transfer is also provided in Table 2, with the difference between the progressive changes in adversarial training. Conventional methods like the Autoencoder, VAE and RNN-LSTM achieve moderate performance in restoring stylistic and tonal characteristics but have a higher Fréchet Audio Distance (FAD) and Spectral Convergence (SC) values, which are perceptual and structural constraints to audio realism.

 Figure 3

Figure 3 Trend Comparison of Model Performance Across Audio Quality Metrics

 

Figure 3 presents the trends of comparative performance of several audio quality evaluation metrics. The FAD and SC of 4.82 and 0.342 of the Autoencoder indicate a rather weak capability to preserve high-level harmonic coherence and the VAE a little more effectively recreates features with more smooth latent representations. The RNN-LSTM has a stronger temporal dependency model, in terms of higher MOS (4.01) and genre consistency (87.2%), but it has a poor timbral richness and variation. Figure 4 presents the trends of multi-metric evaluation between performances of different audio generation models.

 Figure 4

Figure 4 Evaluation of Audio Generation Models Using Multi-Metric Analysis

 

Conversely, the Conditional GAN presents the most promising overall results in all measures since it has an FAD of 2.41, SC of 0.191 and a high Mean Opinion Score of 4.47. These advances verify the higher capacity of GAN to learn intricate distributions, retain timbral elements (91.7%), and stylistic wholeness (92.8%) over transformations.

 

 

8. Conclusion

This study proves the importance of Generative Adversarial Networks (GANs) as a potent platform of musical style transfer and learning to close the divide between computational creativity and human art. The study shows that adversarial learning can be successfully used to learn complicated temporal and spectral correlations found in musical pieces through systematic trial and error using Conditional GANs and CycleGANs. The findings validate that GANs are better than the more traditional deep learning models because they produce musically coherent, stylistically faithful, and perceptually realistic outputs in a wide variety of genres. Mel-spectrograms, MFCCs and chroma-based representations were used as feature engineering which allowed the model to learn complex harmonic, rhythmic, and timbral ones. Cycle-consistency and content-preservation losses were used in adversarial training to find a harmony between musical identity and fidelity to transformation. The architecture that was implemented had a stable convergence, a minimized mode collapse, and outputs that are close to human aesthetic perception. Most importantly, this framework has important implications on AI-assisted composition, interactive music pedagogy, and real-time performance adaptation. It enables composer and students to experiment with genre re-invention, timbre variation and expressive re-interpretation in a manner that stretches the creative frontiers.

 

CONFLICT OF INTERESTS

None. 

 

ACKNOWLEDGMENTS

None.

 

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