Frequency-Domain Audio Resampling for Neural Network Super-Resolution

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Existing audio signal enhancement methods, particularly those using machine learning concepts like deep neural networks, face inefficiencies and increased latency due to the need for pre-processing steps such as upsampling and frequency conversion, which are compute-heavy and inefficient, especially when handling large batches of input signals.

Innovation Solution

An efficient resampling approach using a short-time Fourier transform (STFT) and fast Fourier transform (FFT) resampler that converts lower frequency audio data into the frequency domain, pads higher frequency bands with zero values, and inputs this data into a neural network for audio super-resolution processing, reducing computational overhead and latency.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional time-domain upsampling and frequency conversion is performed before neural network processing, then audio quality enhancement can be achieved, but computational overhead and processing time increase significantly

Engineering Contradiction:

Improveaudio quality enhancementVSAvoidprocessing speed

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The patent inverts the traditional processing sequence by performing frequency conversion before upsampling. Instead of converting to frequency domain after upsampling (which requires heavy computation), the method converts to frequency domain first, then applies lightweight frequency-based upsampling, and only converts back to time domain after neural network processing. This inversion eliminates the need for heavy time-domain upsampling computation.

Inventive Principle:

Principle #13The other way round (Inversion)

Solution Approach 2:

The patent changes the domain parameter from time-domain to frequency-domain representation. By transforming the audio signal into the frequency domain using STFT, the upsampling operation becomes a simple frequency-domain interpolation rather than computationally intensive time-domain convolution, significantly reducing processing complexity and time.

Inventive Principle:

Principle #35Parameter changes

2Ease of manufacture

If fixed frame size resamplers are used to maintain processing consistency, then implementation simplicity is improved, but latency increases acceptably

Engineering Contradiction:

Improveimplementation simplicityVSAvoidlatency

Core Design Contradiction:

Ease of manufactureVSLoss of time

Solution Approach 1:

The patent introduces dynamic frame size adjustment capability to the resampler. Instead of being constrained by fixed frame sizes that cause latency, the system can now process audio frames of varying lengths by dynamically calculating the appropriate frequency-domain representation size. This allows the resampler to adapt to different audio characteristics and reduce unnecessary waiting time while maintaining processing consistency.

Inventive Principle:

Principle #15Dynamics

3Productivity

If frequency-domain resampling with zero-padding is used, then computational efficiency is improved, but frequency resolution may be affected

Engineering Contradiction:

Improvecomputational efficiencyVSAvoidfrequency resolution

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent applies preliminary frequency-domain transformation using STFT before the resampling operation. By converting the audio signal to the frequency domain first and then applying zero-padding for frequency-based upsampling, the system pre-establishes a frequency representation that guides the subsequent resampling process. This preliminary action ensures that frequency resolution is maintained while benefiting from the computational efficiency of frequency-domain operations.

Inventive Principle:

Principle #10Preliminary action

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

This method enables lightweight and fast audio signal enhancement, supporting batch processing and concurrent enhancement of multiple audio streams, while maintaining high quality and reducing processing time, suitable for real-time applications on devices like smartphones.

Implementation Method 1

An efficient resampling approach using a short-time Fourier transform (STFT) and fast Fourier transform (FFT) resampler that converts lower frequency audio data into the frequency domain

Methodology Applied

Scientific EffectShort-time Fourier transform:

Implementation Method 2

Methodology Applied

Scientific EffectFast Fourier transform:

Data Source

PatentUS20240203443A1Efficient frequency-based audio resampling for using neural networks

Publication Date: 2024.06.20 NVIDIA CORP

AI summary

Systems and methods described relate to the enhancement of audio, such as through machine learning-based audio super-resolution processing. An efficient resampling approach can be used for audio data received at a lower frequency than is needed for an audio enhancement neural network. This audio data can be converted into the frequency domain using, and once in the frequency domain (e.g., represented using a spectrogram) this lower frequency data can be resampled to provide a frequency-based representation that is at the target input resolution for the neural network. To keep this resampling process lightweight, the upper frequency bands can be padded with zero value entries (or other such padding values). This resampled, higher frequency spectrogram can be provided as input to the neural network, which can perform an enhancement operation such as audio upsampling or super-resolution.