This advanced deep learning project focuses on speaker recognition by classifying speakers based on the frequency domain representation of their speech, using Fast Fourier Transform (FFT). Built with Python, TensorFlow, and integrated into a Flask web application, it is ideal for final-year engineering students, aligning with IEEE standards and offering significant applications in security and personalization.
Utilizes a 1D Convolutional Neural Network (CNN) with residual connections to classify speakers based on FFT-transformed audio data.
Applies Fast Fourier Transform (FFT) to convert speech samples into the frequency domain for robust feature extraction.
A user-friendly web interface built with HTML, CSS, and JavaScript, powered by Flask, allows users to upload audio samples and receive speaker identification results.
Incorporates background noise to enhance the robustness of the model, ensuring accurate predictions even with noisy inputs.
Achieves high accuracy in speaker identification through custom deep learning models, avoiding reliance on pre-trained models.
Built using Python, TensorFlow 2.3+, and Flask. Requires ffmpeg for resampling audio to 16000 Hz.
Curated dataset of speech samples from multiple speakers, labeled for classification, with background noise augmentation.
Employs a 1D CNN with residual connections, optimized for processing frequency-domain audio data.
Used in authentication systems to grant access based on voice identification.
Enables smart speakers to provide personalized responses based on speaker recognition.
Aids in criminal investigations by identifying individuals from audio evidence.
Enhances call center services by routing calls or offering tailored services based on speaker identity.
When you purchase this project, you gain access to a complete, end-to-end solution designed to ensure your success. Here's what we offer:
Receive fully functional and tested Python code, including the Flask web app, ready for implementation.
We assist in implementing the project on your system, ensuring smooth integration and providing full support throughout the process.
Get detailed documentation, including reports, PPTs, and raw data for research papers, ensuring a successful presentation and publication.
Benefit from ongoing mentorship and support, with assistance for any errors or improvements needed throughout your project journey.
This is one of the best IEEE project ideas for final-year students, combining deep learning, natural language processing, and web development. We provide complete frontend and backend codes, along with detailed explanations to help you understand the project thoroughly. Our support extends to content for your report and IEEE paper publication.