Source-linked AI summary
Audio Super Resolution using Neural Networks
Volodymyr Kuleshov, S. Zayd Enam, Stefano Ermon
TL;DR
Audio super-resolution seeks to reconstruct high-resolution signals from low-resolution audio, addressing the computational and practical challenges of modeling raw audio. The paper learns a conditional model with convolutional architectures, extends image super-resolution to audio, and outperforms prior approaches across speech and music tasks while supporting applications including telephony, compression, and text-to-speech generation.
Problem
Audio processing requires handling more than 10,000 samples per second, while bandwidth extension methods often rely on specialized techniques and hand-crafted features.
Method
The method learns p(y|x) to reconstruct a higher-resolution signal y from a low-resolution signal x using a convolutional model with an additive Gaussian-noise formulation.
Results
The technique outperforms previous bandwidth-extension approaches on speech and non-vocal music and improves over baselines at 2×, 4×, and 6× upscaling ratios.
Takeaways & Limitations
The results demonstrate that feedforward convolutional architectures can perform audio generation without specialized audio processing techniques, with applications in telephony, compression, and text-to-speech generation.
Takeaways & Limitations
On the larger and more diverse MagnaTagATune dataset, the model underfit and showed no improvement over the spline baseline.
Abstract
from arXiv · showhide
We introduce a new audio processing technique that increases the sampling rate of signals such as speech or music using deep convolutional neural networks. Our model is trained on pairs of low and high-quality audio examples; at test-time, it predicts missing samples within a low-resolution signal in an interpolation process similar to image super-resolution. Our method is simple and does not involve specialized audio processing techniques; in our experiments, it outperforms baselines on standard speech and music benchmarks at upscaling ratios of 2x, 4x, and 6x. The method has practical applications in telephony, compression, and text-to-speech generation; it demonstrates the effectiveness of feed-forward convolutional architectures on an audio generation task.
1 INTRODUCTION
The paper targets bandwidth extension with lightweight neural models that reconstruct high-quality audio from low-resolution inputs. It uses feedforward convolutional architectures inspired by image super-resolution and reports practical and generative-modeling significance.
- Raw-audio neural modeling offers flexibility but requires processing more than 10,000 samples per second, motivating lighter approaches.
- Bandwidth extension reconstructs high-quality audio from inputs containing only 15–50% of the original samples.
- The proposed model trains on paired low- and high-quality samples, predicts missing samples at test time, and runs fully feedforward in real time.
- The technique outperforms baselines at 2×, 4×, and 6× upscaling ratios and is simpler than previous methods.
- The approach uses convolutional architectures that scale better with dataset size and computational resources than current alternatives.
- Purely feedforward architectures operating in a non-discretized output space can perform well on an important audio generation task.
2 SETUP AND BACKGROUND
The setup treats digital audio as a sampled time series whose resolution is determined by sampling rate. The task is to predict higher-resolution samples from a fraction of the original signal, extending prior bandwidth-extension approaches.
- An audio signal is modeled as a continuous amplitude function that must be discretized into a vector for digital measurement.
- Sampling rate R is the resolution of an audio vector and ranges from 4 KHz for low-quality telephone speech to 44 Khz for high-fidelity music.
- The paper defines audio super-resolution as predicting a higher-resolution signal from a fraction of the original samples.
- By basic signal processing theory, increasing audio resolution is equivalent to predicting its higher frequencies.
- Earlier bandwidth-extension methods commonly use hand-crafted features and relatively simple models within larger, more complex systems.
3 METHOD
The method reconstructs a higher-resolution signal from a lower-resolution input using a conditional deep convolutional network with residual connections. Its architecture combines bottleneck processing, skip connections, residual learning, and Subpixel upscaling.
- 3.1 SETUP: Given input sampling rate R1 and target rate R2 > R1, the model reconstructs a high-resolution signal y from low-resolution signal x.
- 3.1 SETUP: The model uses upsampling ratios r = 2, 4, and 6 for the two signals.
- 3.1 SETUP: The network learns p(y|x), modeling the higher-resolution signal conditionally on its low-resolution instantiation with Gaussian noise in the basic formulation.
- 3.1 SETUP: Parameters are determined using a mean squared error objective over paired source and target time series.
- Architecture: A deep convolutional network with residual connections implements the mapping from low- to high-resolution audio.
- Bottleneck architecture: The bottleneck architecture alternates downsampling and upsampling blocks, halving spatial dimension during downsampling and reversing this during upsampling.
- Skip connections: Skip connections reuse downsampling features and an additive input-to-output residual connection lets the model learn y − x, speeding training.
- Subpixel shuffling layer: Subpixel layers increase the time dimension by reshuffling convolutional features while preserving tensor entries.
4 EXPERIMENTS
The experiments evaluate audio super-resolution across speech and music tasks, objective and human metrics, architectural choices, computational performance, and domain generalization. The method improves over baselines in standard settings but shows sensitivity to distribution shifts and dataset complexity.
- Datasets and tasks: The evaluation uses VCTK speech and Beethoven piano data, with low-resolution signals generated from 16 KHz originals using an order 8 Chebyshev low-pass filter before subsampling.The tasks include single-speaker, multi-speaker, and piano settings.
- Baselines: The method is compared with cubic B-spline interpolation and a neural baseline that predicts high-frequency STFT phase and magnitude components.The neural baseline uses a dense network with three hidden layers and cannot operate at r = 6 because it requires power-of-two scaling ratios.
- Objective evaluation: Objective metrics improve by 1-5 dB over baselines, with the strongest gains at higher upscaling factors; LSD reveals high-frequency deficiencies that SNR can miss.The method also improves over the dense neural baseline, while the spline baseline can achieve high SNR despite lacking higher frequencies.
- Human evaluation: In a MUSHRA study with 10 subjects and four VCTK recordings at r = 4, the proposed method ranked best among the three upscaling techniques.The study compared original, downsampled, spline, DNN, and proposed-model signals.
- Generalization and limitations: The model produces noisier outputs when training and testing use different low-pass-filter conditions, and switching between speech and music domains also produces noisy output.These results highlight domain adaptation and specialization as practical concerns.
- Architectural analysis: Ablation results show that symmetric skip connections are crucial for good performance, while additive connections provide a smaller perceptible improvement.The ablation uses the MULTISPEAKER task with r = 4 and tracks validation ℓ2 loss over time.
- Computational performance: On 12-second piano inputs, the model processed one second of audio in 0.11s on average on a Titan X GPU, although MULTISPEAKER training required about two days.Unlike sequence-to-sequence models, it can begin producing output without the complete input sequence.
- Generalization and limitations: On the larger and more diverse MagnaTagATune dataset, the model underfit and showed no improvement over the spline baseline.The dataset contains about 200 hours of music spanning 188 genres; the authors expect larger models and more computation could improve results.
5 PREVIOUS WORK AND DISCUSSION
The paper extends conditional modeling ideas from computer vision to aligned time-series problems, proposing convolutional architectures for audio bandwidth extension and related domains. It emphasizes domain-agnostic modeling, scalability, and practical audio applications.
- Time series modeling: The approach generalizes conditional modeling ideas from image super-resolution and colorization to audio time-series reconstruction.It targets source/target series pairs with natural alignment and differences represented by local transformations.
- General architecture: The proposed architecture targets a broad class of aligned conditional time-series problems in signal processing, biomedicine, and other fields.The authors report that the architecture works well across different domains.
- Bandwidth extension: The work proposes the first convolutional architecture for bandwidth extension and reports better scaling with dataset size than current alternatives.It also outperforms recent specialized methods while avoiding many hand-crafted features.
- Bandwidth extension: The bandwidth-extension method is fully domain-agnostic rather than dependent on hand-crafted audio features.This contrasts with existing techniques that use specialized features and methods.
- Audio applications: Applications include higher-rate telephony, compression, text-to-speech generation, and forensic analysis.The work also demonstrates feedforward convolutional architectures on an audio generation task.
6 CONCLUSION
The paper situates audio generation within signal processing and machine learning, then presents audio upsampling as a simple learning-based solution to bandwidth extension. Its method outperforms prior approaches on speech and non-vocal music while supporting practical applications.
- Motivation: Audio generation is a fundamental problem connecting signal processing and machine learning, with applications in recognition, synthesis, recommendation, and other areas.Audio processing also raises basic questions about time series and generative modeling.
- Contribution: The proposed technique upsamples audio by extending image super-resolution ideas to the audio domain.The method uses neural networks to reconstruct higher-quality signals from low-quality inputs.
- Results: The method outperforms previous bandwidth-extension approaches on both speech and non-vocal music.The approach is described as fast and simple to implement.
- Applications: The technique has applications in telephony, compression, and text-to-speech generation.The paper also presents it as evidence that feedforward architectures can work on an important audio generation task.