AI for Audio

Dataset management, labeling, and augmentation; segmentation and feature extraction for audio, speech, and acoustic applications

Audio Toolbox™ provides functionality to develop machine and deep learning solutions for audio, speech, and acoustic applications including speaker identification, speech command recognition, speech separation, acoustic scene recognition, denoising, and many more.

Use audioDatastore to ingest large audio data sets and process files in parallel.
Use Signal Labeler to build audio data sets by annotating audio recordings manually and automatically.
Use audioDataAugmenter to create randomized pipelines of built-in or custom signal processing methods for augmenting and synthesizing audio data sets.
Use audioFeatureExtractor to extract combinations of different features while sharing intermediate computations.

Audio Toolbox also provides access to third-party APIs for text-to-speech and speech-to-text, and it includes pretrained models so that you can perform transfer learning, classify sounds, and extract feature embeddings. Using pretrained networks requires Deep Learning Toolbox™.

Featured Examples

Speech Command Recognition Code Generation on Raspberry Pi

Generate code and deploy feature extraction and speech command recognition network on Raspberry Pi hardware.

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Speech Command Recognition Code Generation with Intel MKL-DNN

Deploy feature extraction and a convolutional neural network (CNN) for speech command recognition on Intel® processors. To generate the feature extraction and network code, you use MATLAB® Coder™ and the Intel® Math Kernel Library for Deep Neural Networks (MKL-DNN). In this example, the generated code is a MATLAB executable (MEX) function, which is called by a MATLAB script that displays the predicted speech command along with the time domain signal and auditory spectrogram. For details about audio preprocessing and network training, see Train Deep Learning Network for Speech Command Recognition.

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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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Denoise Speech Using Deep Learning Networks

Denoise speech signals using deep learning networks. The example compares two types of networks applied to the same task: fully connected, and convolutional.

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Cocktail Party Source Separation Using Deep Learning Networks

Isolate a speech signal using a deep learning network.

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Train Generative Adversarial Network (GAN) for Sound Synthesis

Train and use a generative adversarial network (GAN) to generate sounds.

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Speaker Identification Using Pitch and MFCC

Use machine learning to identify people based on features extracted from recorded speech.

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Speaker Verification Using i-vectors

Speaker verification, or authentication, is the task of confirming that the identity of a speaker is who they purport to be. Speaker verification has been an active research area for many years. An early performance breakthrough was to use a Gaussian mixture model and universal background model (GMM-UBM) [1] on acoustic features (usually mfcc). For an example, see Speaker Verification Using Gaussian Mixture Models. One of the main difficulties of GMM-UBM systems involves intersession variability. Joint factor analysis (JFA) was proposed to compensate for this variability by separately modeling inter-speaker variability and channel or session variability [2] [3]. However, [4] discovered that channel factors in the JFA also contained information about the speakers, and proposed combining the channel and speaker spaces into a total variability space. Intersession variability was then compensated for by using backend procedures, such as linear discriminant analysis (LDA) and within-class covariance normalization (WCCN), followed by a scoring, such as the cosine similarity score. [5] proposed replacing the cosine similarity scoring with a probabilistic LDA (PLDA) model. [11] and [12] proposed a method to Gaussianize the i-vectors and therefore make Gaussian assumptions in the PLDA, referred to as G-PLDA or simplified PLDA. While i-vectors were originally proposed for speaker verification, they have been applied to many problems, like language recognition, speaker diarization, emotion recognition, age estimation, and anti-spoofing [10]. Recently, deep learning techniques have been proposed to replace i-vectors with d-vectors or x-vectors [8] [6].

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Train End-to-End Speaker Separation Model

Use an end-to-end deep learning network for speaker-independent speech separation.

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AI for Audio

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Featured Examples