Audio Processing - MATLAB & Simulink (original) (raw)
Main Content
Extend deep learning workflows with audio and speech processing applications
Apply deep learning to audio and speech processing applications by using Deep Learning Toolbox™ together with Audio Toolbox™. For signal processing applications, see Signal Processing. For applications in wireless communications, see Wireless Communications.
Apps
| Signal Labeler | Label signal attributes, regions, and points of interest, and extract features |
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Functions
Data Management and Augmentation
Feature Extraction
Pretrained Networks
Blocks
VGGish
YAMNet
| YAMNet | YAMNet sound classification network (Since R2021b) |
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| Sound Classifier | Classify sounds in audio signal (Since R2021b) |
OpenL3
CREPE
| CREPE | CREPE deep pitch estimation neural network (Since R2023a) |
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| Deep Pitch Estimator | Estimate pitch with CREPE deep learning neural network (Since R2023a) |
Topics
- Deep Learning for Audio Applications (Audio Toolbox)
Learn common tools and workflows to apply deep learning to audio applications. - Classify Sound Using Deep Learning (Audio Toolbox)
Train, validate, and test a simple long short-term memory (LSTM) to classify sounds. - Adapt Pretrained Audio Network for New Data Using Deep Network Designer
This example shows how to interactively adapt a pretrained network to classify new audio signals using Deep Network Designer. - Audio Transfer Learning Using Experiment Manager
Configure an experiment that compares the performance of multiple pretrained networks applied to a speech command recognition task using transfer learning. - Compare Speaker Separation Models
Compare the performance, size, and speed of multiple deep learning speaker separation models. - Speaker Identification Using Custom SincNet Layer and Deep Learning
Perform speech recognition using a custom deep learning layer that implements a mel-scale filter bank. - Dereverberate Speech Using Deep Learning Networks
Train a deep learning model that removes reverberation from speech. - Sequential Feature Selection for Audio Features
This example shows a typical workflow for feature selection applied to the task of spoken digit recognition. - Train Spoken Digit Recognition Network Using Out-of-Memory Audio Data
This example trains a spoken digit recognition network on out-of-memory audio data using a transformed datastore. - Train Spoken Digit Recognition Network Using Out-of-Memory Features
This example trains a spoken digit recognition network on out-of-memory auditory spectrograms using a transformed datastore. - Investigate Audio Classifications Using Deep Learning Interpretability Techniques
This example shows how to use interpretability techniques to investigate the predictions of a deep neural network trained to classify audio data. - Accelerate Audio Deep Learning Using GPU-Based Feature Extraction
Leverage GPUs for feature extraction to decrease the time required to train an audio deep learning model. - AI for Speech Command Recognition (Audio Toolbox)
Build, train, compress, and deploy a deep learning model for speech command recognition.- STEP 1: Train Deep Learning Network for Speech Command Recognition (Audio Toolbox)
- STEP 2: Prune and Quantize Speech Command Recognition Network (Audio Toolbox)
- STEP 3: Apply Speech Command Recognition Network in Simulink (Audio Toolbox)
- STEP 4: Apply Speech Command Recognition Network in Smart Speaker Simulink Model (Audio Toolbox)
- STEP 5: Deploy Smart Speaker Model on Raspberry Pi (Audio Toolbox)