Audio Signal processing is a method where intensive algorithms, techniques are applied to audio signals. Audio signals are the representation of sound, which is in the form of digital and analog signals. Their frequencies range between 20 to 20,000 Hz, and this is the lower and upper limit of our ears. Analog signals occur in electrical signals, while digital signals occur in binary representations. This process encompasses removing unwanted noise and balancing the time-frequency ranges by converting digital and analog signals. It focuses on computational methods for altering the sounds. It removes or minimizes the overmodulation, echo, unwanted noise by applying various techniques into it.
The "Audio processing" GitHub repository contains comprehensive code for classifying audio using deep learning and the librosa library. The repository is designed for audio processing enthusiasts and developers who want to apply their knowledge in a practical setting. With this repository, you can easily perform audio classification using a deep learning model.
Additionally, the repository includes a user-friendly interface for converting speech to text and vice-versa. This allows you to experiment with speech recognition and conversion in a convenient and accessible way.
Furthermore, the repository also includes code for finding similarities between two audio files. This feature can be useful for a wide range of applications, such as music recommendation systems and audio fingerprinting.
Overall, the "Audio processing" repository provides a complete solution for audio processing and is a valuable resource for anyone looking to explore the field of audio analysis and classification. The repository is well-documented, making it easy to understand and use the code, even if you are new to audio processing.