ICML 21 - Voice2Series: Reprogramming Acoustic Models for Time Series Classification

Related tags

Deep Learning machine-learning deep-learning time-series speech-processing
Overview

Voice2Series-Reprogramming

Voice2Series: Reprogramming Acoustic Models for Time Series Classification

  • International Conference on Machine Learning (ICML), 2021 | Paper | Colab Demo

Environment

Tensorflow 2.2 (CUDA=10.0) and Kapre 0.2.0.

  • Noted: Echo to many interests from the community, we will also provide Pytorch V2S layers and frameworks around this September, incoperating the new torch audio layers. Feel free to email the authors for further collaboration.

  • option 1 (from yml)

conda env create -f V2S.yml
  • option 2 (from clean python 3.6)
pip install tensorflow-gpu==2.1.0
pip install kapre==0.2.0
pip install h5py==2.10.0

Training

  • This is tengible Version. Please also check the paper for actual validation details. Many Thanks!
python v2s_main.py --dataset 0 --eps 100 --mapping 3
  • Result
seg idx: 0 --> start: 0, end: 500
seg idx: 1 --> start: 5000, end: 5500
seg idx: 2 --> start: 10000, end: 10500
Tensor("AddV2_2:0", shape=(None, 16000, 1), dtype=float32)
--- Preparing Masking Matrix
Model: "model_1"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_1 (InputLayer)            [(None, 500, 1)]     0                                            
__________________________________________________________________________________________________
zero_padding1d (ZeroPadding1D)  (None, 16000, 1)     0           input_1[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2 (TensorFlowOp [(None, 16000, 1)]   0           zero_padding1d[0][0]             
__________________________________________________________________________________________________
zero_padding1d_1 (ZeroPadding1D (None, 16000, 1)     0           input_1[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_1 (TensorFlow [(None, 16000, 1)]   0           tf_op_layer_AddV2[0][0]          
                                                                 zero_padding1d_1[0][0]           
__________________________________________________________________________________________________
zero_padding1d_2 (ZeroPadding1D (None, 16000, 1)     0           input_1[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_2 (TensorFlow [(None, 16000, 1)]   0           tf_op_layer_AddV2_1[0][0]        
                                                                 zero_padding1d_2[0][0]           
__________________________________________________________________________________________________
art_layer (ARTLayer)            (None, 16000, 1)     16000       tf_op_layer_AddV2_2[0][0]        
__________________________________________________________________________________________________
reshape_1 (Reshape)             (None, 16000)        0           art_layer[0][0]                  
__________________________________________________________________________________________________
model (Model)                   (None, 36)           1292911     reshape_1[0][0]                  
__________________________________________________________________________________________________
tf_op_layer_MatMul (TensorFlowO [(None, 6)]          0           model[1][0]                      
__________________________________________________________________________________________________
tf_op_layer_Shape (TensorFlowOp [(2,)]               0           tf_op_layer_MatMul[0][0]         
__________________________________________________________________________________________________
tf_op_layer_strided_slice (Tens [()]                 0           tf_op_layer_Shape[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Reshape_2/shape (Te [(3,)]               0           tf_op_layer_strided_slice[0][0]  
__________________________________________________________________________________________________
tf_op_layer_Reshape_2 (TensorFl [(None, 2, 3)]       0           tf_op_layer_MatMul[0][0]         
                                                                 tf_op_layer_Reshape_2/shape[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean (TensorFlowOpL [(None, 2)]          0           tf_op_layer_Reshape_2[0][0]      
==================================================================================================
Total params: 1,308,911
Trainable params: 217,225
Non-trainable params: 1,091,686
__________________________________________________________________________________________________
Epoch 1/100
2021-07-19 01:43:32.690913: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2021-07-19 01:43:32.919343: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
113/113 [==============================] - 6s 50ms/step - loss: 0.0811 - accuracy: 1.0000 - val_loss: 1.5589e-04 - val_accuracy: 1.0000
Epoch 2/100
113/113 [==============================] - 5s 41ms/step - loss: 5.0098e-05 - accuracy: 1.0000 - val_loss: 1.0906e-05 - val_accuracy: 1.0000

Class Activation Mapping

python cam_v2s.py --dataset 5 --weight wNo5_map6-88-0.7662.h5 --mapping 6 --layer conv2d_1

Reference

  • Voice2Series: Reprogramming Acoustic Models for Time Series Classification
@InProceedings{pmlr-v139-yang21j,
  title = 	 {Voice2Series: Reprogramming Acoustic Models for Time Series Classification},
  author =       {Yang, Chao-Han Huck and Tsai, Yun-Yun and Chen, Pin-Yu},
  booktitle = 	 {Proceedings of the 38th International Conference on Machine Learning},
  pages = 	 {11808--11819},
  year = 	 {2021},
  volume = 	 {139},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {18--24 Jul},
  publisher =    {PMLR},
}
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Comments
  • Problem when shifting labels to zero in binary classification problems with negative labels

    Problem when shifting labels to zero in binary classification problems with negative labels

    Hello! I would like to report a possible bug.

    Code: v2s_main.py Lines: 43-45

    When we have multi-class problems, such as ECG 5000 where the original labels are [1,2,3,4,5], the mod function applied in lines 43-45 shifts the labels to zero correctly, so we obtain the values [0,1,2,3,4] as labels.

    However, when we have binary classification with negative labels, such as ECG 200, where the original labels are [-1,1], the mod function used yields 1 as remainder for both cases, and the label vector becomes an array of ones.

    Do you agree?

    opened by DominguesPH 3
  • ECG 200 Data loader error and fixed

    ECG 200 Data loader error and fixed

    Dear Users,

    We get one sincere feedback reported from our user that the ECG 200 dataset was associated with a data loading error.

    We have corrected that error and updated our code with the revised result with 85.5% acc.

    One current state-of-art performance is Nestor Cabello et al., ICDM 2020 with 90.9% acc.

    The other time series does not associate with that error and we have reported the related issues.

    Thank you again for all your feedbacks,

    V2S team

    enhancement 
    opened by huckiyang 0
Owner
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