Welcome !
Students Projects
Where creativity meets innovation!
This is a space dedicated to showcasing the brilliant work and cutting-edge ideas of
our students. Here, you'll find a diverse range of projects that span various fields of study, from technology and
engineering to lastest signal processing and AI technologies
Projects
Fast Learning for Adaptive
Source Separation in New Environments
Description
The project will focus on leveraging deep learning models for source separation, adapting to new environments in real time. This project will aim to improve separation performance in complex and dynamic soundscapes (take a noisy environments, such as overlapping human speakers with a background sound from other human at a distance in a reverb room) using deep learning models like convolutional neural networks (CNNs), recurrent neural networks (RNNs), or transformers.
Audio Classification for
Environmental Monitoring
Description
Your environment is a public park environment and for this create a sound file (fifteen minute duration) that consists a variety of sound sources (such as human talking, calling some one, sound of birds, children are shouting, and fountains' sounds etc.) and identify all the possible sound source present in the environment. Also calculate the percentage of sound sources contribution to the environment for the duration of fifteen minutes (15 min). Use Machine Learning algorithm to and train the model with four different environments.
Build systems capable of
removing unwanted noise from audio signals in real time
Description
You are in a situation where there is a public announcement going on in a very reverb indoor scene (i.e. at airport) and your unwanted noise is background noise (i.e. people are chatting, walking, ambiance sound (pink noise)). You task is to develop an algorithm which remove all the unwanted sound and just reproduce the announcement as clear as possible and with out much loss in the audio quality of the announcement as compared to the original announcement.
Separate mixed audio signals
into individual sources, such as voices and background music
Description
You have a party at your home and your friends are chatting with each other in a back ground musics instrument players. Take a scene where two of the friends are talking (two speech signals) and three of the friends are playing piano, trumpet and guitar simultaneously. You task is to generate such a situation in an audio file as a single channel mixed audio and develop an algorithm to separate each sound source (channel).
Room acoustics simulation
Models implementation in Python such as image source method
Description
Implement a room acoustics simulation tool using the image source method to model sound propagation and reflections in an enclosed room. The simulation will calculate the impulse response of at least three rooms of different sizes and with difference room acoustical treatments (such as, furnished room, empty room, big rooms with acoustical treatment, and big room without acoustical treatment) for different sound sources and receiver positions (at least four source positions and four receiver position), helping to understand how reverberation, absorption and reflections affects of the sound in rooms. Implement frequency-dependent absorption, where the absorption coefficients vary across frequency bands (low, mid, high).
Construct a Room Impulse
Response (RIR) using Ray Tracing (RT) Simulation Methods
Description
Implement a room acoustics simulation tool using the Ray Tracing (RT) methods to model sound propagation and reflections in an enclosed room. The simulation will calculate the impulse response of at least three rooms of different sizes and with difference room acoustical treatments (such as, furnished room, empty room, big rooms with acoustical treatment, and big room without acoustical treatment) for different sound sources and receiver positions (at least four source positions and four receiver position), helping to understand how reverberation, absorption and reflections affects of the sound in rooms. Implement frequency-dependent absorption, where the absorption coefficients vary across frequency bands (low, mid, high). Extend the simulation to handle higher-order reflections more efficiently using ray tracing methods.
A web based tool to implement
audio coders such as FFMPEG to analyze the signals
Description
A web based tool (using Flask, React etc. as a back-end and HTML, JS, CSS) to implement audio coders such as FFMPEG to analyze the signals in terms of bit rates, quantization, sampling and different distortions to analyze the audio effect
Audio source separation with
black box optimization in different reverberation environments
Description
Audio source separation with black-box optimization in reverberant environments involves using optimization algorithms to isolate distinct sound sources from a mixture, without needing detailed knowledge of the environment. In reverberant spaces, where echoes and reflections distort audio, this method adjusts parameters iteratively to improve source separation. By exploring different configurations and evaluating outcomes, black-box optimization can adapt to varying room acoustics and effectively separate desired audio signals, such as speech or music, from unwanted reverberations, even in complex acoustic settings.
Loudspeaker beamforming to
separate two audio of different langages from the main stream of mixed
audio
Description
Loudspeaker beamforming is a technique used to enhance audio signal separation by directing sound energy toward a specific region or listener while minimizing interference from other directions. In the context of separating two audio streams in different languages from a mixed audio signal, beamforming can be employed to spatially isolate the sound sources. By using an array of loudspeakers beamforming algorithms can shape the sound wavefronts, focusing on distinct sound sources based on their spatial position. This enables the separation of audio signals from two distinct language sources, effectively isolating them from the main mixed stream.
Separate mixed audio signals
into individual sources, such as voices and background sounds (take the
cocktail party effect into considerations)
Description
The cocktail party effect, where multiple conversations or noises blend together, highlights the difficulty of isolating a single voice from surrounding distractions. Techniques like blind source separation, spatial filtering, and machine learning are commonly used to separate these sources by analyzing their characteristics, such as their location, frequency patterns, or temporal features. By accounting for the acoustic properties of the environment and leveraging advanced algorithms, these methods aim to improve clarity and intelligibility in mixed audio signals.
Personalized HRTFs (HRIRs)
synthesis using image processing and machine learning. Capture the ear
shape and calculate parameters and select best HRTF from database.
Conduct listening experiment to validate the best personalized HRTFs
Description
Capture the ear shape and calculate parameters required to estimate the HRTF and select best HRTF from database (Arrange dataset of 100 HRTFs from open sources). Conduct MUSHRA listening experiment to validate the best personalized HRTFs. (The Listening experiment must have at least 5 participants and you have to estimate the HRTFs of the individual participants)
Reconstruct missing or
corrupted parts of signals using interpolation or estimation techniques.
Recovering missing samples in audio recordings for seamless playback
Description
In real-world audio recordings, there are often instances where parts of the signal are
missing or corrupted due to factors such as noise, data loss, or hardware limitations. This assignment challenges
students to recover these missing sections, allowing the audio to play back smoothly. Students will learn to
implement interpolation and estimation techniques, such as linear interpolation, spline interpolation, or advanced
machine learning methods, to estimate and fill in the missing audio samples. The process will involve analyzing the
surrounding audio context, selecting the appropriate method for reconstruction, and testing the results to ensure
the reconstructed audio is as natural and seamless as possible.
By the end of this assignment, students will have a working understanding of how to effectively recover missing parts of
audio signals and ensure that the playback remains continuous and natural-sounding.
Analyze sequential data to
identify patterns, trends, and anomalies over time
Description
Develop a Python-based project that uses ARIMA models to forecast time series data (e.g., weather). The goal is to analyze historical data to identify trends, seasonal patterns, and anomalies, and then use this information to make future predictions. After fitting the ARIMA model and forecasting, evaluate the model's performance using error metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), or Root Mean Squared Error (RMSE). Integrate real-time data streams (e.g., weather updates) and make rolling predictions using the ARIMA model.
Process signals received by
sensor arrays to estimate the direction of arrival of incoming signals.
Implementing beamforming techniques for localizing sound sources in
audio recordings
Description
In this project, you will process audio signals received by sensor arrays to estimate the direction of arrival (DOA) of incoming sound sources. By implementing beamforming techniques such as Delay-and-Sum and Minimum Variance Distortionless Response (MVDR), you will localize sound sources in audio recordings, enhancing the signal from the desired direction while reducing interference. The goal is to accurately determine the location of sound sources and improve the clarity of the audio by applying spatial filtering. You will also evaluate the performance of the beamforming methods and visualize the results.
Design Ultrasound Based 3D
Indoor Positioning System
Description
This project involves designing an indoor positioning system that uses ultrasound technology to accurately determine the 3D location of objects or individuals within a defined indoor space. By leveraging the time-of-flight of ultrasound signals between transmitters and receivers, the system can calculate distances and triangulate the position in three dimensions. The project will focus on optimizing signal accuracy, minimizing interference, and implementing real-time tracking. The goal is to create a reliable and efficient solution for indoor navigation, suitable for applications like asset tracking, navigation in large buildings, or augmented reality experiences.
Sound Event Localization and
Detection under general conversation or music listening environments;
such as breaking of glass on floor or window during other voice and
music activities
Description
This project focuses on detecting and localizing specific sound events, such as the breaking of glass, within environments with background noise like conversations or music. By using advanced audio processing and machine learning techniques, the system will differentiate and pinpoint events of interest amidst overlapping sounds. The goal is to accurately identify the occurrence and location of these sound events in real-time, even in dynamic and noisy environments, making it applicable for applications like security systems, emergency response, or smart home technologies.