• AI_GC_Methodology_2018_v1(private)
  • 大戰略
  • Data_Gitlab
    • MATTBN
      • cd1
        • Train_1
          • PTND20011107.name.trs
          • PTSND20011107.trs
      • license.md_MATTBN
    • NER-PhA-Vol1
      • Train_NER-PhA-Vol1
        • data_NER-PhA-Vol1
          • CS_創設市集
            • CS_20160218
              • CS_20160218_001.textgrid
              • CS_20160218_001.txt
              • CS_20160218_001.wav
            • CS_20160324
          • GJ_國際教育新動線
            • GJ_20160208
        • speaker_xlsx_file
      • LICENSE_NER-PHA-Vol1
    • NER-Trs-Vol1
    • NER-Auto-Vol1
    • PTS-MSub-Vol1
  • Taiwanese-Speech-in-the-wild
    • ReadMe.md_file_TSW
  • Neural Machine Translation (seq2seq)
    • train-build_NMT_system
    • Inference-generate_translations
    • Attention-based_NMT_model
    • Tips&Tricks
    • Benchmarks
  • Attention-based Model (李宏毅教授)
    • Attention-based Model (Prof. 李宏毅)
    • Attention-based Model (in RNN ML#21-2 Prof. 李宏毅)
  • Toward Machine Comprehension of Spoken Contents
    • TOFEL
    • Task_and_Contribution
    • Attention-based Multi-hop Recurrent Neural Network (AMRNN) framework
      • AMRNN_framework_v1
    • Experiment_AMRNN
    • Cosine_Similarity
    • bi-directional GRU
  • AI_GC_比賽
    • AI_GC_比賽策略
      • AI_GC_比賽策略_0320_0520 2018
      • AI_GC_比賽策略_0801_0915_2018
    • AI_GC_比賽消息
      • AI語音首支測試檔上線_0608
        • AI語音首支測試檔_0608
          • 又慶錄音部長錄音
      • 科技大擂台與AI對話_正式賽_細部賽制_0524
      • 教育廣播電臺與國立臺北科技大學合作_0424
      • Gitlab開放通知_0411
    • Kaggle1
      • 目錄A_Kaggle1
      • 附錄_Kaggle1
      • AI Grand Challenge 6/23 第一次初賽
        • 導入python packages (6/23比賽)
        • Setup Google Speech-to-Text Service
        • get all choices *.wav files and initialize answer_per_Q
        • Load Audio Files and Sentence Segmentation
          • Inspect Audio Non-silence Intervals
        • Use Google Speech-to-Text API
        • Assign answer_per_Q
        • Save Answers to CSV file
    • Kaggle2
      • Speech Audio Band-pass Filter
      • ai小老師人工校正答案規劃
        • ai 小老師操作細節
          • anaconda install
          • kaggle2_answe_M 資料夾目錄
          • task.py
        • 題目分組
      • 如何使用 Google Cloud Speech-to-Text Service
        • 開通免費試用Google雲端服務
        • Tutorial of Running Google Cloud Speech-to-Text Service on Local Computer
        • Transcription Example from official Github
      • Sentence Segmentation 規劃
        • Match audio
        • Filter and Slience Removal
        • split + noise removal
        • aS.silenceRemoval()
      • Appendix
        • ai小老師人工校正答案規劃架構2
        • Noise Reduction
          • Audacity
          • Human speech frequency band-pass filter
        • Google_Cloud_speect-to-text
          • How-to_Guildes_Google_Cloud
            • Transcribing_long_audio_file
          • QuickStarts_Google_Cloud_speech-to-text
            • QuickStart_using_client_libraries
          • Cloud Speech-to-Text Basics
        • 第一版 手動切 + Google Cloud Speech-to-Text Service
        • 嘗試 Running Google Cloud Speech-to-Text Service on Colab
    • Kaggle3
      • Sentence Segmentation
      • Google Speech-to-Text API Experiment
        • Approach 1: Post-Transcription Keyword based Segmentation
      • Appendix
        • Python Audio APIs
          • PyAudio
          • pyAudioAnalysis3
          • librosa
    • 題目分組細節
    • 團隊資料
    • Python Basic target on AI_GC
      • hidden function definition
      • Module, Package, Library
  • Colab
    • Running Python code in Colab
    • Load external data: Google Drive, Sheets, and Cloud Storage
    • Schematic of colab service and overall process
      • RNN-LSTM example using Colab service
        • reader_test.py_file
          • reader_test.py and reader.py
          • class PtbReaderTest
            • def setUp(self)
            • def testPtbRawData(self)
            • def testPtbProducer(self)
        • reader.py_file
          • from __future__ import absolute_import
          • def _read_words(filename)
          • def _build_vocab(filename)
          • def _file_to_word_ids(filename, word_to_id)
          • def ptb_raw_data(data_path=None)
          • def ptb_producer(raw_data, batch_size, num_steps, name=None)
        • ptb_word_lm.py_file
          • importing_module_RNN_LSTM_example
            • from __future__ import xxxx
            • 相對匯入與絕對匯入
          • class PTBModel (object)
            • __init___RNN_LSTM_example
              • class __init__
              • tf.device() 指定運行設備
              • tf.get_variable 函數的使用
              • tf.nn.embedding_lookup ()
              • tf.nn.dropout ()
              • tf.nn.xw_plus_b()
              • tf.trainable_variables()
              • tf.train.GradientDescentOptimizer()
              • apply_gradients()
            • _build_rnn_graph_RNN_LSTM_example
            • _build_rnn_graph_cudnn_RNN_LSTM_example
            • _get_lstm_cell_RNN_LSTM_example
            • _build_rnn_graph_lstm_RNN_LSTM_example
            • assign_lr_RNN_LSTM_example
            • export_ops_RNN_LSTM_example
            • import_ops_RNN_LSTM_example
          • def main(_)
        • util.py_file
          • def export_state_tuple(state_tuples, name) and def import_state_tuples(state_tuples, name, num_replicas)
          • def with_prefix(prefix, name) and def with_autoparallel_prefix(replica_id, name)
          • class UpdateCollection(object)
            • def __init__(self, meta graph, model)
            • def update_snapshot_name(self, var_coll_name)
            • def replicate_states(self, state_coll_name)
          • def auto_parallel(meta graph, model)
        • __init__.py_file
          • __init__.py file 的用途
        • Appendix 附錄
          • tf.flags
        • Tutorial_Web_RNN_tensorflow
        • GitHub_RNN-LSTM_example
          • Official_models_Tensorflow
          • Tensorflow_Research_Models
          • Samples_in_Tensorflow_Model
      • Vector_Representation_of_words_Tensorflow_Tutorial
        • VRW_Tensorflow_web_tutorial
    • "End-To-End Memory Networks" in Tensorflow
      • README.md
      • data.py
      • main.py
      • model.py
      • utils.py
    • Note_for_Colab
      • 架設colab環境注意事項
    • Colab_疑難雜症
      • Python 2 or Python 3
      • Install_required_modules
    • Colab 系統資源
  • TensorFlow Basic
    • anaconda install
    • TensorFlow Operating Flow
    • Syntax
    • Tensorflow Architecture
    • tensorflow functions
      • tf.concat()
    • TensorFlow 疑難雜症
    • Graphs and Sessions in Detail (宗諭)
  • Supervised and Unsupervised Transfer Learning for Question Answering
    • Supervised_and_unsupervised_transfer_Learning_for_QA_MM_修改
    • Task Descriptions and Approaches
    • Datasets
    • QA Neural Network Models
      • End-to-End Memory Networks
      • Query-Based Attention CNN
    • Question Answering Experiments
      • Supervised Transfer Learning
      • Unsupervised Transfer Learning
  • 參加 Kaggle 比賽
    • Running Python Script
      • Online Kernel
      • Offline Machine
    • 李宏毅老師課程GC練習賽
      • Using Google Cloud Speech-to-Text API
      • Google Speech-to-Text API Example Code
        • gcloud Tutorial
      • Source Data Pre-Processing
  • QACNN
    • QACNN_paper_2017
      • QACNN_Algorithm
      • Experiment_Result_QACNN
      • Discussion_QACNN
    • QACNN Linux 實作
      • QACNN Linux 實作_MM_改
    • QACNN Colab實作
      • preprocess操作細節(宗諭)
      • main操作細節(宗諭)
    • QACNN_github_Coding
      • model/ folder
        • MODEL.py
        • def __init__(self,batch_size,x_dimension,dnn_width,cnn_filterSize,cnn_filterSize2,cnn_filterNum,cnn_filterNum2 ,learning_rate,dropoutRate,choice,max_plot_len,max_len,parameterPath)
          • # initialize parameters #
          • # CNN1 #
          • # CNN2 #
          • # compute #
        • Utility
      • output_data/ folder
        • plot/ folder
        • question/ folder
      • para/ folder
      • preprocess/ folder
        • plot2vec.py
        • qa2vec.py
      • utility/ folder
        • utility.py
          • import_module_batchPadding
          • varSentencePadding_test
          • train-in-utility-py-file
      • raw_data/ folder
        • plot/ folder
        • question/ folder
      • main.py file
  • End-To-End Memory Networks
    • Memory_Network_Approach
  • MovieQA
    • Story Source Data
    • Story JSON Files
    • Benchmark codes
      • data_loader.py
        • def __init__(self)
        • def _populate_xxxx(self)
        • def pprint_xxxx()
        • def get_xxxxx()
      • story_loader.py
        • def __init__(self)
        • def _check_exists(self, filename)
        • def _read_xxxx()
        • def load_story(self, movies_map, story_type='plot')
      • download_dvs_stories.py
    • Python Module Configuration
    • Github Configuration
  • Python Basic associated to MoiveQA
  • 中文speech_recognition
    • Project DeepSpeech
      • How can I train using my own data
    • Kaggle Competition TensorFlow Speech Recognition Challenge
    • Github式介紹中文Speech recognition
      • pannous_tensorflow-speech-recognition
    • SpeechRecognition module
  • 中文word2vector
    • 網頁式介紹實作中文word2vector
      • 使用word2vec訓練中文詞向量
      • 中文word2vector_小實作_Pyladies_Taiwan
      • word2vec詞向量訓練及中文文本相似度計算(壹讀)
      • 用中文資料測試 word2vec
      • word2vec構建中文詞向量
    • Github式介紹中文word2vector
      • Alex-CHUN-YU_Word2vec
        • main_Alex-Chun-yu
        • segmentation_Alex-Chun-yu
        • train_d_py
        • wiki-to-text-py
    • Genism
      • corpora.wikicorpus – Corpus from a Wikipedia dump
    • 科技大擂台介紹文件_wordVector_jeiba
      • 詞向量教學ppt
  • 中文分詞
    • 結巴分詞
      • Load customize dictionary into jieba
    • 使用 JIEBA 結巴中文分詞程式
      • Chinese text segmentation
      • 範例程式碼_jieba
    • Neural_word_segmentation_learning_for_chinese
      • Overview_Algorithm_Word_segmentation
      • Training_CWS
      • Experiment_CWS
      • jcyk_CWS
        • Colab_implement_on_CWS
      • Related_Work_Conclusion
    • LSTM_NN_Chinese_word_segmentation
  • 中文語音Sentence Boundary Detection
    • Text-Domain Sentence Boundary Detection
      • Transcribe_file_with_word_offset Code Implementation
        • Program Environment Setup
        • Butterworth Bandpass Filter
        • function transcribe_file_with_word_time_offsets()
          • Setup Google Cloud Speech-to-Text types.RecognitionConfig
          • Run Google Cloud Speech Recognition
          • Parse Recognition Response and Save Result to excel file
        • Auxiliary Functions
        • Main block of transcribe_file_with_word_offset.py
          • External setup using os.environ and argparse.ArgumentParser
          • Get Processing File List (begin_idx and end_idx)
          • Set output_csv_filename and choices_list
          • Batch-wise Speech Transcription
    • Signal-Domain Sentence Segmentation
      • Match audio Signal (DTW)
      • Silence removal
        • Fixed sound threshold level (librosa.effects.split)
        • Adaptive sound threshold level
          • Fixed level difference to maximum sound level (pydub.silence)
          • Learning sound level using SVM (pyAudioAnalysis.audioSegmentation.silenceRemoval)
    • Acoustic-Domain Sentence Segmentation
      • Syllables-based Sentence Segmentation
      • Automatic Speech Recognition & Assessment (ASRA) Library
  • Google Speech-to-Text
    • Google Cloud Speech-to-Text API using Client Libraries
      • Google Cloud Speech-to-Text API using Client Libraries (on Colab)
    • 實際操作畫面_google_speech_to_text
      • 開通免費Google雲端服務試用Google Cloud Speech API
      • setup Google Cloud Service
      • 使用Google Cloud Speech API
  • Google Cloud Project官網
    • Stackdriver Trace API
    • Google CLOUD SDK
  • Google Cloud Natural Language API Documentation
  • pyAudioAnalysis Library
    • Audio Segmentation
      • Supervised Audio Segmentation
        • Fixed-size-segment Segmentation & Classification
        • KNN-based segmentation & classification
        • HMM-based segmentation & classification
      • Unsupervised Audio Segmentation
        • Silence Removal and Event Detection
        • Speaker Diarization
        • Audio thumbnailing
  • Speech Recognition Wikipedia
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Noise Reduction

Noise Reduction

https://github.com/ZackWalsh57/PythonNoiseReduction [unfinished]

https://github.com/ShinoharaYuuyoru/NoiseReductionUsingGRU [graduation project in BIT, 2018.]

https://github.com/jefflai108/noise_cancellation [casual project, matlab]

https://github.com/dodiku/audio_noise_clustering [An experiment]

https://github.com/dodiku/noise_reduction [An experiment]

300至3000Hz之間的電磁波譜特低頻頻帶也被稱為語音頻率 https://zh.wikipedia.org/wiki/语音频率

ffmpeg -i <input_file> -af "highpass=f=200, lowpass=f=3000" <output_file> https://superuser.com/questions/733061/reduce-background-noise-and-optimize-the-speech-from-an-audio-clip-using-ffmpeg

Audacity

[1] https://manerosss.wordpress.com/2017/07/24/ffmpeg-%C2%B7-apply-a-filter-to-enhance-voice-by-removing-low-and-high-frequency-noises/

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