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aclnet.md

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aclnet

Use Case and High-Level Description

The AclNet model is designed to perform sound classification. The AclNet model is trained on an internal dataset of environmental sounds. For details about the model, see this paper.

The model input is a segment of PCM audio samples in [N, C, 1, L] format.

The model output for AclNet is the sound classifier output for the 53 different environmental sound classes from the internal sound database.

Specification

Metric Value
Type Classification
GFLOPs 1.4
MParams 2.7
Source framework PyTorch*

Accuracy

See this publication and this paper.

Input

Original Model

Audio, name - 0, shape - 1,1,1,L, format is N,C,1,L where:

  • N - batch size
  • C - channel
  • L - number of PCM samples (minimum value is 16000)

Converted Model

Audio, name - 0, shape - 1,1,1,L, format is N,C,1,L where:

  • N - batch size
  • C - channel
  • L - number of PCM samples (minimum value is 16000)

Output

Original Model

Sound classifier (see labels), name - 203, shape - 1,53, output data format is N,C where:

  • N - batch size
  • C - Predicted softmax scores for each class in [0, 1] range

Converted Model

Sound classifier (see labels), name - 203, shape - 1,53, output data format is N,C where:

  • N - batch size
  • C - Predicted softmax scores for each class in [0, 1] range

Legal Information

The original model is distributed under Apache License, Version 2.0.