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

Last update: Jan 03, 2023

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},
}

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

Related tags

Overview

Voice2Series-Reprogramming

Environment

Training

Class Activation Mapping

Reference

Owner

VIsually-Pivoted Audio and(N) Text

Aalto-cs-msc-theses - Listing of M.Sc. Theses of the Department of Computer Science at Aalto University

Sync2Gen Code for ICCV 2021 paper: Scene Synthesis via Uncertainty-Driven Attribute Synchronization

Moiré Attack (MA): A New Potential Risk of Screen Photos [NeurIPS 2021]

[NeurIPS'20] Multiscale Deep Equilibrium Models

Adaptive Denoising Training (ADT) for Recommendation.

Data and code for the paper "Importance of Kernel Bandwidth in Quantum Machine Learning"

MACE is a deep learning inference framework optimized for mobile heterogeneous computing platforms.

Official Repository for the paper "Improving Baselines in the Wild".

Barlow Twins and HSIC

Task-based end-to-end model learning in stochastic optimization

Non-Imaging Transient Reconstruction And TEmporal Search (NITRATES)

Codes for Causal Semantic Generative model (CSG), the model proposed in "Learning Causal Semantic Representation for Out-of-Distribution Prediction" (NeurIPS-21)

OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)

Official Implement of CVPR 2021 paper “Cross-Modal Collaborative Representation Learning and a Large-Scale RGBT Benchmark for Crowd Counting”

BalaGAN: Image Translation Between Imbalanced Domains via Cross-Modal Transfer

This repository provides the official implementation of 'Learning to ignore: rethinking attention in CNNs' accepted in BMVC 2021.

Junction Tree Variational Autoencoder for Molecular Graph Generation (ICML 2018)

This repository contains the code for the CVPR 2020 paper "Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision"

Real-time Object Detection for Streaming Perception, CVPR 2022