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aclnet

Use Case and High-Level Description

The AclNet model is designed to perform sound classification and is trained on internal dataset of environmental sounds for 53 different classes, listed in file <omz_dir>/data/dataset_classes/aclnet.txt. 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 1.42
MParams 2.71
Source framework PyTorch*

Accuracy

Metric Value
Top 1 86.3%
Top 5 92.0%

Metrics were computed on internal validation dataset according to following publication and paper.

Input

Original Model

Audio, name - input, 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 - input, 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 file, <omz_dir>/data/dataset_classes/aclnet_53cl.txt), name - output, 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 file, <omz_dir>/data/dataset_classes/aclnet_53cl.txt), name - output, shape - 1,53, output data format is N,C where:

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

Download a Model and Convert it into Inference Engine Format

You can download models and if necessary convert them into Inference Engine format using the Model Downloader and other automation tools as shown in the examples below.

An example of using the Model Downloader:

python3 <omz_dir>/tools/downloader/downloader.py --name <model_name>

An example of using the Model Converter:

python3 <omz_dir>/tools/downloader/converter.py --name <model_name>

Legal Information

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