Machine learning techniques can be incredibly valuable for signal processing tasks. They can help in extracting meaningful information from various types of signals, such as audio, image, video, and sensor data. By using machine learning algorithms, you can enhance the accuracy and efficiency of signal processing tasks. Some common applications include:

1. Speech Recognition: Machine learning can aid in converting spoken language into written text, enabling voice commands and transcription services.
2. Image and Video Analysis: Machine learning algorithms can be used for image and video processing tasks like object detection, classification, tracking, and scene understanding.
3. Biomedical Signal Processing: Machine learning can help analyze medical signals such as ECG, EEG, and MRI data for diagnosis and monitoring of health conditions.
4. Communication Systems: ML algorithms can enhance signal processing techniques in wireless communication, improving data transfer rates, error correction, and adaptive modulation.
5. Sensor Data Analysis: Machine learning can be used to process data from sensors in various domains like environmental monitoring, industrial automation, and autonomous vehicles.