OpenL3

OpenL3 embeddings extraction network

Since R2022b

• 

Libraries:
Audio Toolbox / Deep Learning

Description

The OpenL3 block leverages a pretrained convolutional neural network that extracts feature embeddings from audio signals. These embeddings are powerful audio representations that can be used for tasks such as classification. This block requires Deep Learning Toolbox™.

Ports

Input

Port_1 — Spectrograms

matrix | 4-D array

Spectrograms generated from audio, specified as an N-by-M matrix or an N-by-M-by-1-by-K array. K represents the number of spectrograms, and N-by-M is the size of the spectrograms and depends on the value of the Spectrum type parameter.

• Mel (128 bands) –– The network accepts mel spectrograms of size 128-by-199, where 128 is the number of mel bands, and 199 is the number of time hops.

• Mel (256 bands) –– The network accepts mel spectrograms of size 256-by-199, where 256 is the number of mel bands, and 199 is the number of time hops.

• Linear –– The network accepts positive one-sided spectrograms of size 257-by-197, where 257 is the FFT length and 197 is the number of time hops.

Data Types: single | double

Output

Port_1 — Embeddings

matrix

Output embeddings, returned as a K-by-L matrix, where K is the number of input spectrograms, and L is specified by the Embedding length parameter.

Data Types: single

Parameters

Spectrum type — Type of spectrum

Mel (128 bands) (default) | Mel (256 bands) | Linear

Type of spectrum generated from audio and used as input to the neural network, specified as Mel (128 bands), Mel (256 bands), or Linear. This parameter specifies the size of the network input Port_1.

Content type — Type of audio content

Environmental sounds (default) | Musical sounds

Type of audio content the neural network was trained on, specified as Environmental sounds or Musical sounds. Set this parameter to Environmental sounds to use a neural network pretrained on environmental audio data, and set it to Musical sounds to use a network pretrained on musical data.

Embedding length — Output embedding length

512 (default) | 6144

Length of output embedding, specified as 512 or 6144.

Mini-batch size — Size of mini-batches

128 (default) | positive integer

Size of mini-batches to use for prediction, specified as a positive integer. Larger mini-batch sizes require more memory but can lead to faster predictions.

Block Characteristics

Data Types	double | single
Direct Feedthrough	no
Multidimensional Signals	no
Variable-Size Signals	no
Zero-Crossing Detection	no

References

[1] Cramer, Jason, et al. "Look, Listen, and Learn More: Design Choices for Deep Audio Embeddings." In ICASSP 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2019, pp. 3852-56. DOI.org (Crossref), doi:/10.1109/ICASSP.2019.8682475.

Extended Capabilities

C/C++ Code Generation

Generate C and C++ code using Simulink® Coder™.

Usage notes and limitations:

• To generate generic C code that does not depend on third-party libraries, in the Configuration Parameters > Code Generation general category, set the Language parameter to C.

• To generate C++ code, in the Configuration Parameters > Code Generation general category, set the Language parameter to C++. To specify the target library for code generation, in the Code Generation > Interface category, set the Target Library parameter. Setting this parameter to None generates generic C++ code that does not depend on third-party libraries.

• For ERT-based targets, the Support: variable-size signals parameter in the Code Generation> Interface pane must be enabled.

• For a list of networks and layers supported for code generation, see Networks and Layers Supported for Code Generation (MATLAB Coder).

Version History

Introduced in R2022b