Authors:
Sean Vasquez, Mike Lewis
Sean Vasquez, Mike Lewis
Description:
The paper presents MelNet, a generative model for audio in the frequency domain. MelNet leverages time-frequency representations (spectrograms) instead of time-domain waveforms, using a highly expressive probabilistic model and a multiscale generation procedure to capture both local and global audio structures.
The paper presents MelNet, a generative model for audio in the frequency domain. MelNet leverages time-frequency representations (spectrograms) instead of time-domain waveforms, using a highly expressive probabilistic model and a multiscale generation procedure to capture both local and global audio structures.
Training and Data:
The model uses an autoregressive approach, factorizing the joint distribution over a spectrogram as a product of conditional distributions. The network is trained to minimize the negative log-likelihood via gradient descent, with separate training for different audio generation tasks.
The model uses an autoregressive approach, factorizing the joint distribution over a spectrogram as a product of conditional distributions. The network is trained to minimize the negative log-likelihood via gradient descent, with separate training for different audio generation tasks.
Advantages:
Captures long-range dependencies more effectively than time-domain models. Generates high-fidelity audio by modeling spectrograms with high temporal and frequency resolution. Applies a multiscale approach to balance capturing global structure and fine-grained details.
Captures long-range dependencies more effectively than time-domain models. Generates high-fidelity audio by modeling spectrograms with high temporal and frequency resolution. Applies a multiscale approach to balance capturing global structure and fine-grained details.
Limitations:
The fidelity of generated audio can be affected by the lossy nature of spectrograms and the chosen spectrogram inversion algorithm. The model’s performance may vary across different audio generation tasks and datasets.
The fidelity of generated audio can be affected by the lossy nature of spectrograms and the chosen spectrogram inversion algorithm. The model’s performance may vary across different audio generation tasks and datasets.
Model Architecture:
The architecture includes a time-delayed stack, frequency-delayed stack, and optionally a centralized stack. The time-delayed stack aggregates information from previous frames, while the frequency-delayed stack processes preceding elements within a frame. The centralized stack operates on entire frames to provide a global context.
The architecture includes a time-delayed stack, frequency-delayed stack, and optionally a centralized stack. The time-delayed stack aggregates information from previous frames, while the frequency-delayed stack processes preceding elements within a frame. The centralized stack operates on entire frames to provide a global context.
Dependencies:
Dependencies include PyTorch for neural network implementation, librosa for audio processing and spectrogram computation, and Griffin-Lim or gradient-based algorithms for spectrogram inversion.
Dependencies include PyTorch for neural network implementation, librosa for audio processing and spectrogram computation, and Griffin-Lim or gradient-based algorithms for spectrogram inversion.
Synthesis:
Uses high-resolution spectrograms for modeling. The autoregressive model generates spectrograms in a coarse-to-fine manner, with the initial tier capturing high-level structure and subsequent tiers adding fine-grained details. Spectrograms are converted back to time-domain signals using spectrogram inversion algorithms.
Uses high-resolution spectrograms for modeling. The autoregressive model generates spectrograms in a coarse-to-fine manner, with the initial tier capturing high-level structure and subsequent tiers adding fine-grained details. Spectrograms are converted back to time-domain signals using spectrogram inversion algorithms.
Dataset:
Four publicly available datasets were used: Blizzard 2013 for single-speaker speech, VoxCeleb2 for multi-speaker speech, MAESTRO for music generation, and TED-LIUM 3 for multi-speaker TTS. Each dataset contains extensive audio recordings with varying characteristics and conditions.
Four publicly available datasets were used: Blizzard 2013 for single-speaker speech, VoxCeleb2 for multi-speaker speech, MAESTRO for music generation, and TED-LIUM 3 for multi-speaker TTS. Each dataset contains extensive audio recordings with varying characteristics and conditions.
Preprocessing:
Preprocessing involves converting audio waveforms to high-resolution spectrograms using the short-time Fourier transform (STFT), applying the Mel scale, and logarithmic rescaling of amplitudes. This process aligns the frequency and amplitude axes with human perception of pitch and loudness, respectively.
Preprocessing involves converting audio waveforms to high-resolution spectrograms using the short-time Fourier transform (STFT), applying the Mel scale, and logarithmic rescaling of amplitudes. This process aligns the frequency and amplitude axes with human perception of pitch and loudness, respectively.
Evaluation Metrics:
Evaluated through subjective human judgments on the long-term structure of generated samples, density estimates for TTS tasks, and qualitative analysis of generated audio samples across different tasks.
Evaluated through subjective human judgments on the long-term structure of generated samples, density estimates for TTS tasks, and qualitative analysis of generated audio samples across different tasks.
Performance:
MelNet generates high-fidelity audio that captures both local characteristics and long-range dependencies, outperforming existing time-domain models like WaveNet in terms of long-term structure and density estimates for TTS tasks. Human evaluations indicate that MelNet-produced samples have more coherent long-term structures.
MelNet generates high-fidelity audio that captures both local characteristics and long-range dependencies, outperforming existing time-domain models like WaveNet in terms of long-term structure and density estimates for TTS tasks. Human evaluations indicate that MelNet-produced samples have more coherent long-term structures.
Contributions:
Introduced MelNet, a generative model for audio that leverages the advantages of spectrogram representations. Demonstrated its effectiveness in capturing long-range dependencies and generating high-fidelity audio across various tasks. Provided a comprehensive evaluation of the model’s performance.
Introduced MelNet, a generative model for audio that leverages the advantages of spectrogram representations. Demonstrated its effectiveness in capturing long-range dependencies and generating high-fidelity audio across various tasks. Provided a comprehensive evaluation of the model’s performance.