Signal Processing
Digital signal analysis, frequency domain transforms, and audio processing with NumPy, SciPy, and advanced DSP techniques
Digital Signal Processing Expertise
Leveraging advanced signal processing techniques to analyze, transform, and extract meaningful information from time-series data. Specializing in frequency domain analysis, filtering, and real-time audio processing applications.
Fast Fourier Transform Analysis
Time-Frequency Decomposition
Audio Processing Pipeline
Technical Implementation
📊 Frequency Domain Analysis
Utilizing NumPy's FFT implementation for efficient frequency spectrum analysis. Transform time-domain signals to identify dominant frequencies, harmonics, and spectral patterns.
Key Capabilities:
- Fast Fourier Transform (FFT) and inverse FFT (IFFT)
- Power spectral density estimation
- Spectrogram generation for time-frequency visualization
- Frequency filtering and band-pass operations
🌊 Wavelet Transform Analysis
Implementing discrete and continuous wavelet transforms using PyWavelets and SciPy for multi-resolution signal analysis. Ideal for non-stationary signals where frequency content changes over time.
Applications:
- Transient signal detection and analysis
- Multi-scale feature extraction
- Signal denoising with wavelet thresholding
- Time-frequency localization for event detection
🔊 Digital Filtering & Audio Processing
Designing and implementing IIR and FIR filters using SciPy.signal for noise reduction, frequency isolation, and signal conditioning. Real-time audio processing pipelines for streaming data.
Filter Types:
- Butterworth, Chebyshev, and Elliptic filters
- Band-pass, band-stop, low-pass, and high-pass filters
- Adaptive filtering for noise cancellation
- Real-time convolution and FIR filtering
🔬 Signal Analysis & Feature Extraction
Extracting meaningful features from signals for classification, anomaly detection, and pattern recognition. Statistical and spectral features for machine learning pipelines.
Features & Techniques:
- Statistical moments (mean, variance, skewness, kurtosis)
- Spectral features (centroid, rolloff, flux, bandwidth)
- Zero-crossing rate and autocorrelation analysis
- Mel-frequency cepstral coefficients (MFCC) for audio
Real-World Applications
🎵 Audio Analysis
Music information retrieval, pitch detection, tempo estimation, and audio fingerprinting for content identification and recommendation systems.
📈 Financial Time-Series
Trend analysis, cycle detection, and noise filtering for stock market data. Spectral analysis for identifying periodic patterns in trading signals.
🏥 Biomedical Signals
ECG and EEG signal processing for healthcare applications. Filtering artifacts, detecting anomalies, and extracting diagnostic features from physiological data.
📡 Communication Systems
Modulation/demodulation, channel equalization, and interference mitigation. Implementing software-defined radio (SDR) techniques for signal transmission.
Technology Stack
Core Libraries
- •NumPy
Array operations and FFT implementation
- •SciPy.signal
Filter design and signal processing algorithms
- •PyWavelets
Wavelet transform implementation
Visualization
- •Matplotlib
Time-domain and frequency-domain plots
- •Plotly
Interactive spectrograms and 3D visualizations
- •Librosa
Audio feature extraction and visualization
Interested in Signal Processing Solutions?
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