{"data":{"kind":"file","path":"README.md","version_id":"ayswjyhbkia5mzmrdx726exz","entry":{"name":"README.md","path":"README.md","is_directory":false,"size":2639,"modified_at":"2026-09-04T02:46:05.067000","content_hash":"55ef2f597515a690546ab88166fd9dff10656c4a61e2b937463e8e25cb8abea0"},"entries":[],"content":"# Audio Signal Processing & Noise Reduction\n\nA comprehensive verifiers environment for evaluating LLM knowledge of digital audio signal processing, noise reduction techniques, and acoustic engineering.\n\n## Overview\n\nThis environment tests deep understanding of audio DSP concepts through 15 carefully crafted tasks spanning the full breadth of audio signal processing:\n\n- **Spectral Analysis**: FFT/DFT fundamentals, STFT, time-frequency tradeoffs, windowing functions\n- **Noise Characterization**: White/pink/brown noise, SNR measurement, dBA weighting, noise floor concepts\n- **Filter Design**: FIR vs IIR filters, Butterworth/Chebyshev/Elliptic responses, bilinear transform, windowed-sinc design\n- **Noise Reduction Algorithms**: Spectral subtraction, Wiener filtering, musical noise artifacts, decision-directed SNR estimation\n- **Adaptive Filtering**: LMS/NLMS algorithms, convergence analysis, active noise cancellation\n- **Wavelet Denoising**: DWT, multiresolution analysis, hard/soft thresholding, Donoho-Johnstone procedure\n- **Perceptual Coding**: Psychoacoustic masking, MDCT, MP3/AAC comparison, bit allocation\n- **Room Acoustics**: Sabine equation, RT60, image source method, Schroeder/FDN reverb\n- **Pitch Detection**: Autocorrelation, YIN, cepstral methods, polyphonic challenges\n- **Dynamic Range Processing**: Compressor/limiter design, sidechain filtering, loudness war\n- **Digital Audio Fundamentals**: Nyquist theorem, aliasing, quantization, dithering, sigma-delta ADC\n- **Spatial Audio**: Beamforming, microphone arrays, ICA/BSS\n- **Audio Restoration**: Click removal, wow/flutter correction, de-clipping\n- **Deep Learning for Audio**: Conv-TasNet, DCCRN, SI-SNR loss, masking vs mapping\n\n## Evaluation Criteria\n\nResponses are scored across 5 dimensions:\n\n| Dimension | Weight | Description |\n|-----------|--------|-------------|\n| Technical Accuracy | 30% | Core DSP concepts, mathematical formulations, precise terminology |\n| Completeness | 25% | Coverage of all question aspects, sufficient detail |\n| Structured Explanation | 15% | Clear organization, logical flow, proper formatting |\n| Practical Insight | 15% | Real-world applications, tradeoffs, implementation considerations |\n| Domain Depth | 15% | Advanced concepts, named references, cross-disciplinary connections |\n\n## Usage\n\n```python\nimport verifiers as vf\n\nenv = vf.load_environment(\"audio-signal-processing-noise-reduction\")\n# or\nfrom audio_signal_processing import load_environment\nenv = load_environment()\n```\n\n## Version History\n\n- **v1.0.1** — Initial release with 15 comprehensive tasks covering all major audio DSP topics\n\n## License\n\nMIT\n","encoding":"utf-8","truncated":false,"total_bytes":2639},"status":null}