Data Augmentation

Augmentation pipelines, shift pitch and time, stretch time, control volume and noise

Enlarge audio data sets by creating custom augmentation pipelines specific to audio, such as pitch shifting, time shifting, and time stretching. Use theaudioDataAugmenterobject to perform combinations of augmentations probabilistically or deterministically and in series or in parallel. TheaudioDataAugmenterobject also enables you to define your own custom augmentations.

Objects

`audioDataAugmenter`	Augment audio data
`audioTimeScaler`	Apply time scaling to streaming audio

Functions

`shiftPitch`	Shift audio pitch
`stretchAudio`	Time-stretch audio

Featured Examples

Keyword Spotting in Noise Using MFCC and LSTM Networks

Identify a keyword in noisy speech using a deep learning network. In particular, the example uses a Bidirectional Long Short-Term Memory (BiLSTM) network and mel frequency cepstral coefficients (MFCC).

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Speech Emotion Recognition

Illustrates a simple speech emotion recognition (SER) system using a BiLSTM network. You begin by downloading the data set and then testing the trained network on individual files. The network was trained on a small German-language database [1].

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Acoustic Scene Recognition Using Late Fusion

Create a multi-model late fusion system for acoustic scene recognition. The example trains a convolutional neural network (CNN) using mel spectrograms and an ensemble classifier using wavelet scattering. The example uses the TUT dataset for training and evaluation [1].

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Time-Frequency Masking for Harmonic-Percussive Source Separation

Time-frequency masking is the process of applying weights to the bins of a time-frequency representation to enhance, diminish, or isolate portions of audio.

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Dereverberate Speech Using Deep Learning Networks

Train a deep learning model that removes reverberation from speech.

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Acoustics-Based Machine Fault Recognition

Develop a deep learning model to detect faults in an air compressor and package the system to operate on streaming data.

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