オーディオ処理

Learn common tools and workflows to apply deep learning to audio applications.

Train, validate, and test a simple long short-term memory (LSTM) to classify sounds.

This example shows how to interactively adapt a pretrained network to classify new audio signals using Deep Network Designer.

Configure an experiment that compares the performance of multiple pretrained networks applied to a speech command recognition task using transfer learning.

Compare the performance, size, and speed of multiple deep learning speaker separation models.

Perform speech recognition using a custom deep learning layer that implements a mel-scale filter bank.

Train a deep learning model that removes reverberation from speech.

この例では、数字の音声認識タスクに適用される特徴選択の標準的なワークフローを説明します。

This example trains a spoken digit recognition network on out-of-memory audio data using a transformed datastore. In this example, you apply a random pitch shift to audio data used to train a convolutional neural network (CNN). For each training iteration, the audio data is augmented using the audioDataAugmenter (Audio Toolbox) object and then features are extracted using the audioFeatureExtractor (Audio Toolbox) object. The workflow in this example applies to any random data augmentation used in a training loop. The workflow also applies when the underlying audio data set or training features do not fit in memory.

This example trains a spoken digit recognition network on out-of-memory auditory spectrograms using a transformed datastore. In this example, you extract auditory spectrograms from audio using audioDatastore (Audio Toolbox) and audioFeatureExtractor (Audio Toolbox), and you write them to disk. You then use a signalDatastore (Signal Processing Toolbox) to access the features during training. The workflow is useful when the training features do not fit in memory. In this workflow, you only extract features once, which speeds up your workflow if you are iterating on the deep learning model design.

This example shows how to use interpretability techniques to investigate the predictions of a deep neural network trained to classify audio data.

Leverage GPUs for feature extraction to decrease the time required to train an audio deep learning model.

Build, train, compress, and deploy a deep learning model for speech command recognition.

• ステップ 1: Train Deep Learning Network for Speech Command Recognition (Audio Toolbox)
• ステップ 2: Prune and Quantize Speech Command Recognition Network (Audio Toolbox)
• ステップ 3: Apply Speech Command Recognition Network in Simulink (Audio Toolbox)
• ステップ 4: Apply Speech Command Recognition Network in Smart Speaker Simulink Model (Audio Toolbox)
• ステップ 5: Deploy Smart Speaker Model on Raspberry Pi (Audio Toolbox)