Beyond Human Voices: Synthesizing Singing with Any Timbre

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Author: Denis Avetisyan

Researchers have developed a new framework that allows for the generation of singing voices with non-human characteristics, opening possibilities for creative vocal expression and unique audio experiences.

CartoonSing facilitates both the synthesis of non-human singing voices directly from musical scores and the conversion of existing audio into novel vocal performances, demonstrating a unified framework for manipulating and generating singing styles beyond the human realm.
CartoonSing facilitates both the synthesis of non-human singing voices directly from musical scores and the conversion of existing audio into novel vocal performances, demonstrating a unified framework for manipulating and generating singing styles beyond the human realm.

CartoonSing utilizes a two-stage synthesis pipeline and disentangled content representation to address data scarcity and alignment challenges in non-human vocalization, enabling timbre transfer for singing voice generation.

While significant progress has been made in singing voice synthesis and conversion, current systems remain largely confined to replicating human vocal timbres. This limitation motivates the work presented in ‘CartoonSing: Unifying Human and Nonhuman Timbres in Singing Generation’, which introduces a novel framework for generating musically coherent singing with non-human vocal characteristics. CartoonSing addresses the challenges of data scarcity and timbral disparity through a two-stage pipeline that disentangles content representation from vocal timbre. Could this approach unlock new creative possibilities for virtual characters, gaming, and broader audio entertainment experiences?

Beyond the Human Voice: Deconstructing Sonic Identity

For decades, the field of voice synthesis prioritized the accurate reproduction of human vocal qualities. This focus, while achieving remarkable realism in speech and song, inadvertently constrained creative exploration. Existing techniques often treated the voice as a fixed entity, inextricably linking musical expression to a specific human timbre and style. Consequently, generating truly novel vocal performances – those distinctly non-human in character – proved challenging. The inherent limitations of replicating existing voices restricted the potential for sonic innovation, effectively capping the artistic range of synthesized singing and hindering the creation of entirely new vocal aesthetics. This pursuit of human-like fidelity, while technically impressive, ultimately served as a barrier to unlocking the full expressive power of digital voice generation.

The field of singing voice synthesis is undergoing a transformative shift with the emergence of Non-Human Singing Generation (NHSG). This innovative approach moves beyond the traditional goal of replicating human vocal characteristics, instead focusing on the creation of expressive singing voices that deliberately explore timbres and styles outside the realm of human capability. Researchers are actively developing systems capable of generating vocals possessing qualities like metallic resonance, ethereal textures, or even those reminiscent of natural soundscapes – effectively broadening the sonic palette available to musicians and artists. NHSG isn’t simply about creating different voices; it’s about unlocking entirely new modes of musical expression, potentially giving rise to vocal performances previously unimaginable and challenging conventional notions of what singing can be. This frontier promises not just technical advancements, but a reimagining of vocal artistry itself.

The pursuit of non-human singing demands a fundamental rethinking of how singing voice is generated. Traditional methods, built upon replicating human vocal characteristics, prove inadequate for creating truly novel sounds; therefore, researchers are developing new techniques for both content representation – how musical elements like pitch, duration, and dynamics are encoded – and timbre control – the ability to shape the unique sonic qualities of a voice. Effective content representation moves beyond simply mirroring human performance, allowing for the creation of melodies and phrasing unbound by human limitations. Simultaneously, advanced timbre control methods are crucial, enabling the manipulation of vocal qualities – from breathiness and resonance to formant structures – to generate sounds that exist entirely outside the realm of human vocal production, fostering a truly expansive landscape for musical creativity.

The creation of truly non-human singing necessitates a fundamental separation of what is sung from who – or what – is singing it. Current voice synthesis often binds musical content-pitch, rhythm, and lyrics-to the characteristics of a specific vocal identity. To move beyond imitation, researchers are developing techniques to disentangle these elements, allowing for independent control over expression and timbre. This decoupling unlocks unprecedented artistic possibilities; a composer could, for example, imbue a melancholic melody with the resonant quality of a cello, or a soaring chorus with the ethereal texture of wind chimes, effectively creating vocal performances originating from entirely new sonic sources. By treating vocal identity as a malleable parameter, rather than a fixed attribute, Non-Human Singing Generation promises a future where the boundaries of musical expression are redefined, venturing far beyond the limitations of the human voice.

Conventional and non-human singing voice synthesis and conversion differ in their task formulations, impacting how singing voice is generated and modified.
Conventional and non-human singing voice synthesis and conversion differ in their task formulations, impacting how singing voice is generated and modified.

Decoding the Song: Content Representation as Foundation

High-fidelity singing synthesis relies fundamentally on an accurate digital representation of the underlying musical content. This representation must capture essential elements such as melody, harmony, rhythm, and musical structure, as these constitute the core information a synthesis model uses to generate vocals. Inaccuracies or omissions in this representation directly translate to errors in the synthesized performance, including incorrect pitches, timing issues, or harmonically inappropriate notes. Consequently, a robust content representation is not merely a preprocessing step, but a critical determinant of overall singing quality; a synthesis system can only reproduce what it accurately perceives within the input musical data.

ContentVec and HuBERT are prominent models utilized for converting symbolic music data – such as MIDI or music notation – into sequences of discrete tokens. ContentVec employs a vector quantization approach to learn a codebook of representative musical segments, mapping each segment to a corresponding token. HuBERT, originally designed for speech recognition, applies a masked prediction objective; it learns to predict masked portions of the symbolic music sequence, generating contextualized token representations. Both methods effectively reduce the complexity of musical data while retaining essential structural information, enabling efficient processing by downstream synthesis models. The resulting discrete token sequences serve as a compact and informative content representation, capturing musical elements like melody, harmony, and rhythm without being tied to specific audio characteristics.

The Score Representation Encoder utilizes a Transformer architecture to convert symbolic music scores, such as MIDI or MusicXML, into a sequence of frame-level acoustic features. This process involves embedding the symbolic tokens – representing pitch, duration, and other musical events – and then processing these embeddings through multiple layers of self-attention and feed-forward networks. The Transformer learns to capture long-range dependencies within the musical score, enabling the generation of acoustic features that reflect the overall musical structure. The resulting frame-level acoustic representation provides a time-varying signal suitable for conditioning a vocoder or other speech synthesis modules, effectively translating the musical content into an audio-realizable form. The output dimensionality of this representation is configurable, allowing for trade-offs between compression and the fidelity of the acoustic information preserved.

The resulting content representations, derived from models like ContentVec or the Score Representation Encoder, are designed to capture the essential musical structure – melody, harmony, and rhythm – while explicitly excluding information related to vocal performance characteristics such as vibrato, articulation, or vocal texture. This decoupling is achieved through the use of discrete tokens or frame-level acoustic features that prioritize musical elements and minimize dependence on vocal timbre. Consequently, these representations are significantly lower-dimensional than raw audio waveforms, offering a compact and efficient means of encoding musical content. This allows for manipulation of the musical structure – such as key transposition or harmonic variation – without altering the underlying vocal characteristics, and conversely, enables the application of different vocal timbres to the same musical content.

The proposed synthesis pipeline utilizes a two-stage process, first training an encoder on human singing data and then a unified vocoder on both human and non-human audio to generate synthesized sound.
The proposed synthesis pipeline utilizes a two-stage process, first training an encoder on human singing data and then a unified vocoder on both human and non-human audio to generate synthesized sound.

Sculpting the Sonic Palette: Neural Vocoders and Timbre Embeddings

Timbre embeddings are low-dimensional vector representations of the spectral envelope of an audio signal, effectively capturing the qualities that distinguish different instruments or voices. These embeddings are generated by analyzing the frequency content of audio frames and condensing this information into a fixed-length vector, typically ranging from 64 to 512 dimensions. This compact representation allows for efficient storage and manipulation of sonic characteristics; for example, interpolating between two timbre embeddings can create a smooth transition between the corresponding sounds. By decoupling spectral information from the core speech or musical content, timbre embeddings enable independent control over the tonal color of an audio signal, facilitating applications such as voice cloning, instrument transformation, and the creation of novel audio textures.

Several neural network architectures have proven effective for generating timbre embeddings from raw audio. RawNet3 utilizes a multi-layer perceptron to learn representations from filterbank energies, demonstrating robustness to noise. AudioMAE, based on masked autoencoding, learns compressed representations by reconstructing masked portions of the input spectrogram. CLAP (Contrastive Language-Audio Pre-training) leverages a contrastive learning approach, training an encoder to align audio embeddings with text descriptions, resulting in embeddings that capture perceptually relevant acoustic features. Each model provides a distinct methodology for reducing the dimensionality of audio signals while preserving information critical for timbre representation.

The Timbre-Aware Vocoder utilizes the BigVGAN-v2 architecture to generate audio waveforms conditioned on three primary inputs: frame-level spectral representations, fundamental frequency ($F_0$) contours, and timbre embeddings. This approach allows for independent control over the audio’s spectral content, pitch, and tonal characteristics. Specifically, the vocoder processes these inputs to reconstruct the waveform, leveraging the BigVGAN-v2’s generative capabilities to synthesize realistic audio. The frame-level representations capture the short-time spectral characteristics, $F_0$ defines the pitch contour, and the timbre embeddings, derived from models like RawNet3, provide a compact representation of the audio’s unique tonal color, enabling timbre transfer and manipulation.

Adversarial training is a key component in refining the output of the Timbre-Aware Vocoder, contributing to increased naturalness in synthesized audio. This technique involves a discriminator network that evaluates the vocoder’s output, providing feedback used to improve waveform generation. Evaluations consistently demonstrate performance gains in timbre similarity, as quantified by the SIM-A metric, which leverages the VGGish model for perceptual audio analysis. Specifically, the adversarial training process effectively minimizes the perceptual distance between synthesized and ground-truth audio, resulting in a more realistic and natural sound quality.

CartoonSing: A Framework for Unleashing Non-Human Voices

CartoonSing establishes a streamlined pipeline for non-human singing synthesis by tightly integrating two core components: a Score Representation Encoder and a Timbre-Aware Vocoder. The encoder distills musical scores into a condensed, informative representation of the song’s content – encompassing melody, rhythm, and phrasing – effectively separating what is sung from how it is sung. This encoded representation is then fed into the Timbre-Aware Vocoder, which reconstructs the audio signal, imbuing it with a chosen vocal timbre. By decoupling content and timbre in this way, the framework offers a flexible architecture capable of both synthesizing singing from scratch and converting existing performances into new vocal styles, creating a unified approach to non-human singing voice generation and manipulation. This cohesive design is crucial for achieving nuanced control over the final output and producing expressive, diverse singing performances.

CartoonSing presents a significant advancement by consolidating two previously distinct approaches to non-human vocal performance: singing voice synthesis (NHSVS) and singing voice conversion (NHSVC). Traditionally, NHSVS generates singing voices from scratch, requiring extensive data for each desired character or effect, while NHSVC modifies existing human singing to resemble a non-human timbre. This framework bypasses the limitations of both methods by treating content – the melody and lyrics – separately from timbre – the unique sonic qualities defining a voice. Consequently, CartoonSing can both create entirely new non-human singing performances and transform existing ones, offering a unified pipeline for a wider range of applications and creative possibilities. The integration streamlines the process, enabling researchers and artists to explore diverse vocal expressions without being constrained by the data requirements or limitations of individual synthesis or conversion techniques.

CartoonSing introduces a novel approach to non-human singing voice synthesis by fundamentally separating the content of a song – its melody and lyrics – from its timbre, or the unique sonic qualities defining a particular voice. This decoupling allows for an exceptional degree of control over the generated singing voice; users can effectively swap, modify, or combine different timbres without altering the core musical performance. The framework achieves this through a Score Representation Encoder and Timbre-Aware Vocoder, enabling the creation of singing voices with characteristics previously unattainable in automated systems. Consequently, the system can synthesize a human-sung melody with the timbre of a cartoon character, an animal, or even a completely artificial sound, offering unprecedented versatility in generating expressive and diverse non-human singing performances and pushing the boundaries of voice manipulation technology.

CartoonSing delivers a remarkably adaptable system for generating non-human singing, achieving nuanced and varied vocal performances. Evaluations demonstrate significant improvements in accurately replicating the characteristic sound – or timbre – of non-human voices, as evidenced by higher scores in both SIM-A and SIM-S metrics. Importantly, the framework also shows gains in maintaining the quality of human vocal characteristics when applied to human voices. However, performance isn’t uniform; reported F0 RMSE values, alongside instances of output failure indicated by “NaN”, suggest that precise pitch control remains a challenge, particularly when synthesizing certain vocal qualities and demanding further refinement of the system’s pitch accuracy.

The development of CartoonSing exemplifies a willingness to dismantle established norms in singing voice generation. The framework doesn’t simply accept the limitations of existing datasets – particularly the scarcity of non-human vocalizations – but actively seeks to circumvent them through a two-stage synthesis pipeline. This mirrors the philosophy espoused by Grace Hopper: “It’s easier to ask forgiveness than it is to get permission.” CartoonSing, in its innovative approach to disentangled content representation and timbre transfer, essentially asks forgiveness for challenging conventional methods, prioritizing exploration and the achievement of novel vocal qualities over strict adherence to established procedures. The system’s success hinges on a fundamental questioning of ‘how things are done,’ revealing a deeper understanding of the underlying principles governing vocal synthesis.

Beyond Mimicry: The Future of Vocal Synthesis

CartoonSing successfully navigates the predictable problem of limited data in non-human vocalization – a clever workaround, predictably. However, the very success of timbre transfer begs a more disruptive question: how much of ‘singing’ is intrinsically linked to the human vocal tract, and how much is merely a pattern that can be produced by any sufficiently malleable sound source? The framework demonstrates an ability to mimic, but genuine synthesis demands an exploration of vocal qualities independent of existing examples – a move from replication to creation.

Future work will likely focus on disentangling content representation further, perhaps moving beyond current limitations in controlling nuanced vocal characteristics. Yet, a more fruitful avenue lies in deliberately introducing ‘errors’ or ‘imperfections’ into the synthesis – not as bugs to be fixed, but as opportunities to discover novel aesthetic qualities. A perfectly rendered imitation reveals nothing; a controlled deviation might expose the underlying principles governing vocal expressivity itself.

Ultimately, the true challenge isn’t to make machines sing like us, but to understand what ‘singing’ fundamentally is – a question best approached not through refinement, but through a systematic dismantling of preconceptions. The black box has been opened; now, the real work begins – taking it apart, piece by piece.

Original article: https://arxiv.org/pdf/2511.21045.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-12-01 05:33