Arjun Pankajakshan, Ph.D Email & Phone Number

United States

• Audio Deepfake Detection
• Speaker verification/ identification

• My research area is Computational Audio Analysis (Machine Listening) applied to urban, everyday, healthcare, bio-acoustic, and nature sounds. In particular, my work is focused on improving sound recognition using audio.
• Developed CRNN-based Multi-task Learning models, incorporating sound activity and onset detection as auxiliary tasks, which improved sound event detection performance in UrbanSED and DCASE datasets.
• Formulated self-attention mechanisms for sound event sequences, enabling analysis at the audio frame, segment, and event levels.
• Developed a time-restricted self-attention-based SED model, showcasing the benefits of local attention context in modelling temporal relationships in sound event sequences.
• Designed and implemented Transformer Encoder-based sound event detection models with adaptive and dynamic context selection, and a source-separation functionalities within the multi-head attention unit.

London, England, United Kingdom

• Developed a real-time bird audio detection system capable of classifying a large set of bird species, experimenting with up to 200 classes.
• Utilised the Scaper audio library to generate synthetic audio samples to add more variations to the dataset.
• Employed pre-trained audio models like PANNs, VGGish, and L3-Net for fine-tuning the bird audio model.

London, England, United Kingdom

• Replicated VITS-based TTS model results on LJ Speech and VCTK datasets.
• Preprocessed the Spotify podcast dataset for TTS model development, involving data cleansing, segmentation, tokenization, normalization, formatting, silence removal, and audio feature extraction.
• Explored challenges and variations in developing and evaluating a TTS model using the Spotify podcast dataset, including investigating different text and audio durations.
• Explored spectrogram-based objective evaluation methods for TTS models.

India

• Contributed to bird data (image and video) collection and annotation.
• Developed a fully convolutional model for bird audio detection that achieved comparable performance to the winning entry of the BAD challenge 2017.
• Developed a Faster RCNN-CNN based two-stage image model to enhance bird-part localization in images. The first-stage model is exclusively trained using bird-part images, contributing to the refinement of the.
• Developed a pre-processing technique for temporally localizing bird activity frames in long-duration recorded video data by leveraging a pretrained bird image model.

Doctor Of Philosophy - Phd, Computational Audio Analysis, Deep Learning

Master Of Technology (Mtech), Communication Engineering & Signal Processing

Engineer’S Degree, Electronics And Communications Engineering, First Class

Matriculation, Science