Table of Contents

Class NeuralNoiseReducer<T>

Namespace
AiDotNet.Audio.Enhancement
Assembly
AiDotNet.dll

Neural network-based noise reducer for high-quality audio enhancement.

public class NeuralNoiseReducer<T> : AudioNeuralNetworkBase<T>, INeuralNetworkModel<T>, INeuralNetwork<T>, IFullModel<T, Tensor<T>, Tensor<T>>, IModel<Tensor<T>, Tensor<T>, ModelMetadata<T>>, IModelSerializer, ICheckpointableModel, IParameterizable<T, Tensor<T>, Tensor<T>>, IFeatureAware, IFeatureImportance<T>, ICloneable<IFullModel<T, Tensor<T>, Tensor<T>>>, IGradientComputable<T, Tensor<T>, Tensor<T>>, IJitCompilable<T>, IInterpretableModel<T>, IInputGradientComputable<T>, IDisposable, IAudioEnhancer<T>

Type Parameters

T

The numeric type used for calculations.

Inheritance
NeuralNoiseReducer<T>
Implements
IFullModel<T, Tensor<T>, Tensor<T>>
IModel<Tensor<T>, Tensor<T>, ModelMetadata<T>>
IParameterizable<T, Tensor<T>, Tensor<T>>
ICloneable<IFullModel<T, Tensor<T>, Tensor<T>>>
IGradientComputable<T, Tensor<T>, Tensor<T>>
Inherited Members
Extension Methods

Remarks

This model uses an encoder-bottleneck-decoder architecture inspired by U-Net to learn the mapping from noisy audio to clean audio. It operates in the time-frequency domain using STFT for analysis and synthesis. Note: The current implementation uses a simplified dense decoder; a full U-Net with transposed convolutions and skip connections is planned for future versions.

For Beginners: This is like a "magic eraser" for audio noise!

How it works:

  1. Converts audio to a spectrogram (picture of sound)
  2. Neural network learns to identify and remove noise patterns
  3. Converts cleaned spectrogram back to audio

Key features:

  • Works on any type of noise (AC hum, fan noise, traffic, etc.)
  • Preserves speech/music quality while removing noise
  • Can be trained on your specific noise conditions
  • Supports real-time streaming for live applications

Use cases:

  • Podcast/video production (remove background noise)
  • Voice calls (improve speech clarity)
  • Music restoration (remove hiss/crackle from old recordings)
  • Hearing aids (enhance speech in noisy environments)

Two modes of operation:

  1. ONNX Mode: Load a pre-trained model for instant use
  2. Native Mode: Train your own model on custom data

Constructors

NeuralNoiseReducer(NeuralNetworkArchitecture<T>, int, int, int, int, int, int, int, double, ILossFunction<T>?)

Creates a NeuralNoiseReducer in native training mode.

public NeuralNoiseReducer(NeuralNetworkArchitecture<T> architecture, int sampleRate = 16000, int fftSize = 512, int hopSize = 256, int numChannels = 1, int numStages = 4, int baseFilters = 32, int bottleneckDim = 256, double enhancementStrength = 0.8, ILossFunction<T>? lossFunction = null)

Parameters

architecture NeuralNetworkArchitecture<T>

The neural network architecture (user-defined for full customization).

sampleRate int

Audio sample rate (default: 16000).

fftSize int

FFT window size (default: 512).

hopSize int

Hop size between frames (default: 256).

numChannels int

Number of audio channels (default: 1).

numStages int

Number of encoder/decoder stages (default: 4).

baseFilters int

Base filter count (default: 32).

bottleneckDim int

Bottleneck hidden dimension (default: 256).

enhancementStrength double

Enhancement strength 0-1 (default: 0.8).

lossFunction ILossFunction<T>

Loss function for training (default: L1 loss).

Remarks

For Beginners: Use this constructor when you want to train your own model. You'll need noisy/clean audio pairs for training.

NeuralNoiseReducer(NeuralNetworkArchitecture<T>, string, int, int, int, int, double)

Creates a NeuralNoiseReducer in ONNX inference mode using a pre-trained model.

public NeuralNoiseReducer(NeuralNetworkArchitecture<T> architecture, string modelPath, int sampleRate = 16000, int fftSize = 512, int hopSize = 256, int numChannels = 1, double enhancementStrength = 0.8)

Parameters

architecture NeuralNetworkArchitecture<T>

The neural network architecture (user-defined for full customization).

modelPath string

Path to the ONNX model file.

sampleRate int

Audio sample rate (default: 16000).

fftSize int

FFT window size (default: 512).

hopSize int

Hop size between frames (default: 256).

numChannels int

Number of audio channels (default: 1).

enhancementStrength double

Enhancement strength 0-1 (default: 0.8).

Remarks

For Beginners: Use this constructor when you have a pre-trained model. Just point to the ONNX file and start enhancing audio immediately.

Properties

EnhancementStrength

Gets or sets the enhancement strength (0.0 = no enhancement, 1.0 = maximum).

public double EnhancementStrength { get; set; }

Property Value

double

Remarks

Higher values provide more noise reduction but may introduce artifacts. Start with 0.5-0.7 for natural-sounding results.

LatencySamples

Gets the processing latency in samples.

public int LatencySamples { get; }

Property Value

int

Remarks

Important for real-time applications. Lower latency means faster response but potentially lower quality enhancement.

NumChannels

Gets the number of audio channels supported.

public int NumChannels { get; protected set; }

Property Value

int

Methods

CreateNewInstance()

Creates a new instance of the same type as this neural network.

protected override IFullModel<T, Tensor<T>, Tensor<T>> CreateNewInstance()

Returns

IFullModel<T, Tensor<T>, Tensor<T>>

A new instance of the same neural network type.

Remarks

For Beginners: This creates a blank version of the same type of neural network.

It's used internally by methods like DeepCopy and Clone to create the right type of network before copying the data into it.

DeserializeNetworkSpecificData(BinaryReader)

Deserializes network-specific data that was not covered by the general deserialization process.

protected override void DeserializeNetworkSpecificData(BinaryReader reader)

Parameters

reader BinaryReader

The BinaryReader to read the data from.

Remarks

This method is called at the end of the general deserialization process to allow derived classes to read any additional data specific to their implementation.

For Beginners: Continuing the suitcase analogy, this is like unpacking that special compartment. After the main deserialization method has unpacked the common items (layers, parameters), this method allows each specific type of neural network to unpack its own unique items that were stored during serialization.

Enhance(Tensor<T>)

Enhances audio quality by reducing noise and artifacts.

public Tensor<T> Enhance(Tensor<T> audio)

Parameters

audio Tensor<T>

Input audio tensor with shape [channels, samples] or [samples].

Returns

Tensor<T>

Enhanced audio tensor with the same shape as input.

EnhanceWithReference(Tensor<T>, Tensor<T>)

Enhances audio with a reference signal for echo cancellation.

public Tensor<T> EnhanceWithReference(Tensor<T> audio, Tensor<T> reference)

Parameters

audio Tensor<T>

Input audio (microphone signal).

reference Tensor<T>

Reference audio (speaker playback signal).

Returns

Tensor<T>

Enhanced audio with echo removed.

Remarks

For Beginners: This is for video calls!

The problem: Your microphone picks up sound from your speakers, creating an echo for the other person.

Solution: We know what's playing from the speakers (reference), so we can subtract it from what the microphone picks up.

EstimateNoiseProfile(Tensor<T>)

Estimates the noise profile from a segment of audio.

public void EstimateNoiseProfile(Tensor<T> noiseOnlyAudio)

Parameters

noiseOnlyAudio Tensor<T>

Audio containing only noise (no signal).

Remarks

For Beginners: Some enhancers work better if you tell them what the noise sounds like. Record a few seconds of "silence" (just the background noise) and pass it here.

GetModelMetadata()

Gets the metadata for this neural network model.

public override ModelMetadata<T> GetModelMetadata()

Returns

ModelMetadata<T>

A ModelMetaData object containing information about the model.

InitializeLayers()

Initializes the layers of the neural network based on the architecture.

protected override void InitializeLayers()

Remarks

For Beginners: This method sets up all the layers in your neural network according to the architecture you've defined. It's like assembling the parts of your network before you can use it.

PostprocessOutput(Tensor<T>)

Postprocesses model output into the final result format.

protected override Tensor<T> PostprocessOutput(Tensor<T> modelOutput)

Parameters

modelOutput Tensor<T>

Raw output from the model.

Returns

Tensor<T>

Postprocessed output in the expected format.

Predict(Tensor<T>)

Makes a prediction using the neural network.

public override Tensor<T> Predict(Tensor<T> input)

Parameters

input Tensor<T>

The input data to process.

Returns

Tensor<T>

The network's prediction.

Remarks

For Beginners: This is the main method you'll use to get results from your trained neural network. You provide some input data (like an image or text), and the network processes it through all its layers to produce an output (like a classification or prediction).

PreprocessAudio(Tensor<T>)

Preprocesses raw audio for model input.

protected override Tensor<T> PreprocessAudio(Tensor<T> rawAudio)

Parameters

rawAudio Tensor<T>

Raw audio waveform tensor [samples] or [batch, samples].

Returns

Tensor<T>

Preprocessed audio features suitable for model input.

Remarks

For Beginners: Raw audio is just a series of numbers representing sound pressure. Neural networks often work better with transformed representations like mel spectrograms. This method converts raw audio into the format the model expects.

ProcessChunk(Tensor<T>)

Processes audio in real-time streaming mode.

public Tensor<T> ProcessChunk(Tensor<T> audioChunk)

Parameters

audioChunk Tensor<T>

A small chunk of audio for real-time processing.

Returns

Tensor<T>

Enhanced audio chunk (may have latency).

Remarks

For real-time applications like video calls. The enhancer maintains internal state between calls for continuity.

ResetEnhancerState()

Resets the enhancer streaming state.

public void ResetEnhancerState()

SerializeNetworkSpecificData(BinaryWriter)

Serializes network-specific data that is not covered by the general serialization process.

protected override void SerializeNetworkSpecificData(BinaryWriter writer)

Parameters

writer BinaryWriter

The BinaryWriter to write the data to.

Remarks

This method is called at the end of the general serialization process to allow derived classes to write any additional data specific to their implementation.

For Beginners: Think of this as packing a special compartment in your suitcase. While the main serialization method packs the common items (layers, parameters), this method allows each specific type of neural network to pack its own unique items that other networks might not have.

Train(Tensor<T>, Tensor<T>)

Trains the neural network on a single input-output pair.

public override void Train(Tensor<T> input, Tensor<T> expected)

Parameters

input Tensor<T>

The input data.

expected Tensor<T>

Remarks

This method performs one training step on the neural network using the provided input and expected output. It updates the network's parameters to reduce the error between the network's prediction and the expected output.

For Beginners: This is how your neural network learns. You provide: - An input (what the network should process) - The expected output (what the correct answer should be)

The network then:

  1. Makes a prediction based on the input
  2. Compares its prediction to the expected output
  3. Calculates how wrong it was (the loss)
  4. Adjusts its internal values to do better next time

After training, you can get the loss value using the GetLastLoss() method to see how well the network is learning.

UpdateParameters(Vector<T>)

Updates the network's parameters with new values.

public override void UpdateParameters(Vector<T> parameters)

Parameters

parameters Vector<T>

The new parameter values to set.

Remarks

For Beginners: During training, a neural network's internal values (parameters) get adjusted to improve its performance. This method allows you to update all those values at once by providing a complete set of new parameters.

This is typically used by optimization algorithms that calculate better parameter values based on training data.

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