This project introduces VOCOS, audio synthesis a Python based framework aimed at bridging the divide between time-domain and Fourier-based neural vocoders for advanced audio. Existing systems predominantly rely on either time-domain or Fourier-based approaches, each possessing its strengths and limitations. VOCOS, on the other hand, integrates both techniques, leveraging their respective advantages to achieve high-quality audio with improved realism and flexibility

  • Existing System

Current audio systems often operate exclusively in either the time domain or frequency domain, limiting their ability to capture complex audio nuances. This project addresses this gap by proposing a unified approach that combines the strengths of both domains for enhanced audio.

  • Proposed System

VOCOS proposes a hybrid neural vocoder architecture that seamlessly integrates time-domain and Fourier-based methods. This integration is achieved through novel neural network architectures and training strategies, allowing the model to capture and reproduce intricate details in audio signals.

  • Hardware Requirements: Standard computing hardware with sufficient processing power (e.g., multicore CPU, GPU for accelerated training).
  • Software Requirements: Python programming language, deep learning frameworks (e.g., TensorFlow, PyTorch), and relevant libraries for signal processing.
  • Architecture

The VOCOS architecture consists of dual pathways, one dedicated to processing time-domain information and the other to frequency-domain data. A neural network fusion layer combines the features extracted from both domains, allowing the model to generate high-quality synthetic audio with improved realism.

  • Technologies Used
  • Programming Language: Python
  • Deep Learning Frameworks: TensorFlow, PyTorch
  • Signal Processing Libraries: Librosa, NumPy
  • Web User Interface Technologies: Flask, HTML, CSS, JavaScript (for potential integration with a web-based interface)

Web User Interface

A user-friendly web interface built using Flask, HTML, CSS, and JavaScript provides access to VOCOS. This interface provides users with the ability to input audio data, configure synthesis parameters, and visualize the generated audio outputs. This user-centric approach aims to make advanced audio synthesis accessible to a broader audience.

In conclusion, VOCOS signifies an innovative approach to audio synthesis, seamlessly unifying time-domain and Fourier-based methods incorporation of these techniques is anticipated to result in a substantial elevation of the realism and quality of synthetic audio outputs. The project’s web interface ensures user-friendly interaction, making VOCOS a valuable tool for researchers, musicians, and audio enthusiasts.

audio synthesis,vocos-closing-the-gap-between-time-domain-and-fourier-based-neural-vors-for-high-quality, final year projects
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