pyClarity

Getting Started:

  • Installation
    • PyPi
    • GitHub
      • Pip GitHub install
      • Manual Cloning and Installation
  • Usage
    • Jupyter Notebooks
    • 01 Installing pyClarity and Using Metadata
      • The structure of the metadata files
      • Processing Collections of Scenes
      • Associations between metadata types
      • Example: Locating information about the scene’s listener
    • 02 Running the CEC2 Baseline from the command line
      • Install Demo Data
    • Inspecting Existing Configuration
    • 03 Running the CEC2 Baseline from Python
    • Running the baseline
      • Importing the baseline NALR and Compressor components
      • Configuring the NALR and Compressor components
      • IMPORTANT
      • Selecting a scene and a listener
      • Loading the signals to process
      • Applying the NALR and Compressor components
    • Evaluating outputs using HASPI

API Documentation:

  • Enhancer
    • compressor
    • nalr
    • Dnn
      • mc_conv_tasnet
    • Dsp
      • filter
    • Gha
      • gainrule_camfit
      • gha_interface
      • gha_utils
    • Multiband Compressor
      • compressor_qmul
      • crossover
      • multiband_compressor
  • Evaluator
    • Haaqi
      • haaqi
    • Haspi
      • eb
      • ebm
      • haspi
      • ip
    • Hasqi
      • hasqi
    • Mbstoi
      • mbstoi
      • mbstoi_utils
    • Msbg
      • cochlea
      • msbg
      • msbg_utils
      • smearing
  • Predictor
    • torch_msbg
    • torch_stoi
  • Data
    • HOA_tools_cec2
    • demo_data
    • scene_builder_cec2
    • scene_renderer_cec1
    • scene_renderer_cec2
    • utils
  • Dataset
    • cec1_dataset
  • Utils
    • audiogram
    • file_io
    • flac_encoder
    • results_support
    • signal_processing
    • source_separation_support
    • Car Noise Simulator
      • carnoise_parameters_generator
      • carnoise_signal_generator

Cadenza Challenge Recipes:

  • CAD2
    • Task1
      • Convtasnet
        • eval
        • train
        • Local
      • Baseline
        • enhance
        • evaluate
    • Task2
      • Convtasnet
        • eval
        • train
        • Local
      • Baseline
        • enhance
        • evaluate
        • merge_batches_results
      • Process Dataset
        • generate_train_scenes
        • process_zenodo_download
  • ICASSP 2024
    • Baseline
      • enhance
      • evaluate
      • merge_batches_results
    • Generate Dataset
      • generate_at_mic_musdb18
      • generate_train_scenes
  • CAD1
    • Task1
      • Baseline
        • enhance
        • evaluate
        • merge_batches_results
        • test
    • Task2
      • Baseline
        • audio_manager
        • baseline_utils
        • car_scene_acoustics
        • enhance
        • evaluate
        • merge_batches_results
        • test
      • Data Preparation
        • build_scene_metadata

Clarity Challenge Recipes:

  • CPC3
    • Baseline
      • compute_haspi
      • evaluate
      • predict_dev
      • predict_train
      • shared_predict_utils
  • CEC3
    • Baseline
      • enhance
      • evaluate
      • report_score
      • Hydra
        • Launcher
  • CPC2
    • Baseline
      • compute_haspi
      • evaluate
      • predict
  • ICASSP 2023
    • Baseline
      • enhance
      • evaluate
      • report_score
  • CEC2
    • Baseline
      • enhance
      • evaluate
      • Data Generation
        • build_additional_scenes
        • render_additional_scenes
        • Hydra
    • Data Preparation
      • build_scenes
      • render_scenes
      • Hydra
        • Launcher
  • CPC1
    • Baseline
      • compute_scores
      • run
    • E029 Sheffield
      • evaluate
      • infer
      • prepare_data
      • train_asr
      • transformer_cpc1_ensemble_decoder
    • E032 Sheffield
      • evaluate
      • infer
      • prepare_data
      • train_asr
      • transformer_cpc1_decoder
  • CEC1
    • Baseline
      • enhance
      • evaluate
    • Data Preparation
      • prepare_cec1_data
    • E009 Sheffield
      • test
      • train

Contributing:

  • Contributing to pyClarity
    • Using pyClarity
    • Contributing Code
      • Use a Virtual Environment
      • Install Development Dependencies
      • Create an issue
      • Create a Fork
    • Coding Style
      • pre-commit
      • pathlib over os
      • IDEs
    • Testing
  • Contributor Covenant Code of Conduct
    • Our Pledge
    • Our Standards
    • Enforcement Responsibilities
    • Scope
    • Enforcement
    • Enforcement Guidelines
      • 1. Correction
      • 2. Warning
      • 3. Temporary Ban
      • 4. Permanent Ban
    • Attribution
pyClarity
  • Enhancer
  • Dnn
  • View page source

Dnn

Python Modules

clarity.enhancer.dnn.mc_conv_tasnet

Adopted from https://github.com/kaituoxu/Conv-TasNet
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