SP CUP Phase 2 - Audio Source Separation with DCCRN

Angle-conditioned audio source separation using a DCCRN architecture for IEEE Signal Processing Cup 2026.

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📁 Project Structure

SP_CUP_Phase_2/
├── Dataset Generation/              # MATLAB scripts for synthetic dataset creation
│   ├── train_anechoic.m             # Training data (150k samples, RT60=0.0)
│   ├── train_reverb.m               # Training data (150k samples, RT60=0.5)
│   ├── test_anechoic.m              # Test data (5k samples, fixed 90°/40° angles)
│   └── test_reverb.m                # Test data (5k samples, fixed 90°/40° angles)
│
├── Model Inference/                 # Python training, testing, and inference
│   ├── train_Conformer.py           # Training script
│   ├── test_Conformer.py            # Evaluation script (SI-SDR, STOI, PESQ)
│   ├── inference_Conformer.py       # Single-file inference
│   ├── anechoic_Conformer.pth       # Trained model (anechoic)
│   ├── reverb_Conformer.pth         # Trained model (reverberant)
│   ├── evaluation_anechoic/         # Evaluation outputs
│   └── evaluation_reverb/           # Evaluation outputs
│
├── Submission/                      # Self-contained competition submission
│   ├── Task1_Anechoic/
│   │   ├── Task1_Anechoic_5dB.mat
│   │   ├── anechoic_Conformer.pth
│   │   ├── process_task1.py
│   │   └── [audio files]
│   └── Task2_Reverberant/
│       ├── Task2_Reverberant_5dB.mat
│       ├── reverb_Conformer.pth
│       ├── process_task2.py
│       └── [audio files]
│
├── prepare_submission.m             # Generates submission folder from evaluation
├── requirements.txt                 # Python dependencies
└── README.md

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🔧 Requirements

Python

pip install -r requirements.txt

Package	Version	Purpose
torch	2.6.0	Deep learning
torchaudio	2.6.0	Audio I/O
torchmetrics	1.8.2	PESQ, STOI, SI-SDR
soundfile	latest	Audio backend
pesq, pystoi	latest	Metrics

MATLAB

• MATLAB R2020b+
• Signal Processing Toolbox
• Parallel Computing Toolbox
• rir_generator MEX function

RIR Generator Setup

The rir_generator MEX function needs to be compiled before running dataset generation:

% 1. Navigate to RIR_gen folder
cd RIR_gen

% 2. Configure MEX compiler for C++
mex -setup

% Select a C++ compiler (MinGW-w64, MSVC, etc. must be installed)

% 3. Compile the RIR generator
mex rir_generator.cpp rir_generator_core.cpp

Note: On Windows, install MinGW-w64 or Visual Studio with C++ build tools. On Linux/macOS, ensure g++ or clang++ is available.

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🚀 Pipeline

1. Download Raw Dataset

Download the raw audio files needed for dataset generation:

1. Download Dataset_raw.zip from Google Drive
2. Place the zip file in the project root (SP_CUP_Phase_2/)
3. Extract it so the folder structure looks like:

SP_CUP_Phase_2/
└── Dataset_raw/
    ├── Male/       # Male speech files (.wav/.flac)
    ├── Female/     # Female speech files (.wav/.flac)
    ├── Noise/      # Noise files (.wav/.flac)
    └── Music/      # Music files (.wav/.flac)

Note: Dataset_raw/ and Dataset_raw.zip are gitignored and will not be committed.

2. Dataset Generation (MATLAB)

cd "Dataset Generation"
train_anechoic   % 150k samples, RT60=0.0, random angles
train_reverb     % 150k samples, RT60=0.5, random angles
test_anechoic    % 5k samples, RT60=0.0, fixed angles (90°/40°)
test_reverb      % 5k samples, RT60=0.5, fixed angles (90°/40°)

Output per sample:

sample_XXXXX/
├── mixture.wav      # Stereo (target + interferer + noise)
├── target.wav       # Ground truth
├── interference.wav # Interference
└── meta.json        # {target_angle, interf_angle, rt60, ...}

Settings: SIR=0dB, SNR=5dB, 16kHz, 4s duration

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3. Training

cd "Model Inference"
python train_Conformer.py

Edit config in script:

DATASET_ROOT = r"../Train_Dataset/reverb"  # or anechoic
RESUME_FROM = "reverb_Conformer.pth"       # or None

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4. Evaluation

cd "Model Inference"
python test_Conformer.py

Edit config:

MODEL_PATH = "anechoic_Conformer.pth"
TEST_DATASET_ROOT = r"../Test_Dataset/anechoic"
OUTPUT_DIR = "evaluation_anechoic"

Outputs: Best samples by category (Overall, Male+Female, Male+Music, Male+Noise)

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5. Single-File Inference

python inference_Conformer.py -i input.wav -a 90 -o output.wav -m reverb_Conformer.pth -d cuda

Arg	Description
-i	Input stereo audio
-a	Target angle (0-180°)
-o	Output file
-m	Model checkpoint
-d	Device (cpu/cuda)

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6. Generate RIR Data for Submission

matlab -batch "run('generate_rir_data.m')"

Generates rir_data.mat containing Room Impulse Responses for:

• Anechoic (RT60 = 0.0)
• Reverberant (RT60 = 0.5)

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7. Generate Submission

matlab -batch "run('prepare_submission.m')"

Creates self-contained Submission/ folder ready for competition.

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🏗️ Model Architecture

DCCRNConformer (~10M parameters)

Component	Details
Encoder	Complex Conv2d: 2→48→96→192→256
Bottleneck	Dual-Path Conformer (3 blocks, 4 heads)
Decoder	Complex ConvTranspose2d with skip connections
Conditioning	Angle MLP injection at bottleneck

Audio: 16kHz, STFT n_fft=512, hop=128, 3s fixed input

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� Evaluation Results

Anechoic Condition (5,000 samples)

Category	SI-SDR (dB)	STOI	PESQ
Best Overall	16.91	0.950	2.64
Male + Noise	16.91	0.950	2.64
Male + Music	13.46	0.956	2.54
Male + Female	12.96	0.959	2.64

Inference: 50.6ms avg (59x real-time for 3s audio)

Reverberant Condition (5,000 samples)

Category	SI-SDR (dB)	STOI	PESQ
Best Overall	12.49	0.942	2.48
Male + Noise	12.62	0.850	2.00
Male + Music	11.58	0.886	2.27
Male + Female	12.49	0.942	2.48

Inference: 50.5ms avg (59x real-time for 3s audio)

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�📊 Metrics

Metric	Description
SI-SDR	Scale-Invariant Signal-to-Distortion Ratio (dB)
STOI	Short-Time Objective Intelligibility (0-1)
PESQ	Perceptual Evaluation of Speech Quality (-0.5 to 4.5)

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📋 Quick Start

# Setup
cd SP_CUP_Phase_2
pip install -r requirements.txt

# Inference
cd "Model Inference"
python inference_Conformer.py -i audio.wav -a 90 -o out.wav -d cuda

# Evaluate
python test_Conformer.py

# Generate submission
cd ..
matlab -batch "run('prepare_submission.m')"

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License

Developed for IEEE Signal Processing Cup 2026 competition.