MelNet: A Generative Model for Audio in the Frequency Domain (1906.01083v1)

Abstract: Capturing high-level structure in audio waveforms is challenging because a single second of audio spans tens of thousands of timesteps. While long-range dependencies are difficult to model directly in the time domain, we show that they can be more tractably modelled in two-dimensional time-frequency representations such as spectrograms. By leveraging this representational advantage, in conjunction with a highly expressive probabilistic model and a multiscale generation procedure, we design a model capable of generating high-fidelity audio samples which capture structure at timescales that time-domain models have yet to achieve. We apply our model to a variety of audio generation tasks, including unconditional speech generation, music generation, and text-to-speech synthesis---showing improvements over previous approaches in both density estimates and human judgments.

• The paper introduces a novel autoregressive spectrogram-based model that effectively captures long-range audio dependencies.
• It leverages a multiscale generation process to refine coarse spectrograms into detailed, globally coherent audio representations.
• Human evaluations reveal that MelNet outperforms traditional time-domain models like WaveNet in speech and music synthesis quality.

An Expert Review of "MelNet: A Generative Model for Audio in the Frequency Domain"

The presented paper, "MelNet: A Generative Model for Audio in the Frequency Domain," introduces a novel approach to generating audio using spectrogram representations, diverging from traditional time-domain methodologies. The MelNet model capitalizes on two-dimensional time-frequency representations, successfully capturing long-range dependencies that have been a challenge for existing time-domain models like WaveNet and SampleRNN.

Core Contributions and Technical Innovation

The primary innovation of MelNet lies in its ability to model longer-range dependencies by operating on spectrograms, which are more compact along the temporal axis compared to time-domain signals. Leveraging this advantage, MelNet employs an expressive autoregressive model and a multiscale generation procedure. This combination enables MelNet to generate high-fidelity audio that preserves both local and global structures—an achievement unattainable by prior end-to-end time-domain models without intermediate feature conditioning.

The architecture of MelNet integrates autoregressive processes across both time and frequency dimensions, employing a multiscale approach to construct spectrograms initially at a coarse level, refining them iteratively to add finer details. This multiscale procedure enables the generation of spectrograms that maintain global consistency while capturing the nuances necessary for audio fidelity. MelNet's use of dense autoregressive models ensures a comprehensive representation of audio, from preserving the richness of speech tones to the intricate patterns of music compositions.

Experimental Insights and Evaluation

The authors applied the MelNet model across diverse audio generation tasks, including unconditional speech and music generation, along with text-to-speech (TTS) synthesis. In unconditional tasks, MelNet demonstrated the ability to produce speech samples that exhibit coherent linguistic structures such as phonemes and words, alongside preserving the prosody and speaker characteristics over extended timeframes. Moreover, in realms of music generation using the MAESTRO dataset, MelNet samples showed consistent melody and harmony, showcasing the model's grasp of musical structure.

The paper outlines quantitative evaluations using a human listener paper to compare MelNet with WaveNet. Participants overwhelmingly favored MelNet’s output for demonstrating superior long-range structural coherence. Furthermore, MelNet was shown to outperform a two-stage Wave2Midi2Wave model for music generation, elucidating the efficacy of its end-to-end generative modeling without relying on intermediate MIDI representations.

Implications and Future Directions

The implications of MelNet are substantial in both theoretical and practical dimensions of AI-driven audio synthesis. The ability to generate high-fidelity audio with global coherence positions MelNet as a valuable tool for applications in TTS systems, music production, and potentially other domains where audio generation is pivotal. By not requiring intermediate linguistic features, MelNet simplifies the synthesis pipeline for TTS and opens pathways for fully end-to-end generative modeling.

Future developments could explore the integration of MelNet with advancements in neural vocoders to further enhance audio quality. Additionally, further optimizing the autoregressive mechanisms could lead to more efficient models, enabling real-time applications and expanding the domains where MelNet can be effectively deployed.

In summary, MelNet stands as a significant contribution towards bridging the gap between time-domain and frequency-domain modeling of audio. By adeptly leveraging spectrograms through innovative autoregressive and multiscale methodologies, MelNet pioneers advancements in comprehensive audio synthesis, charting a course for future research and practical applications in AI audio generation.