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Found 28 papers, 2 papers with code
Beyond Human Vision: The Role of Large Vision Language Models in Microscope Image Analysis
VLMs such as LLaVA, ChatGPT-4, and Gemini have recently shown impressive performance on tasks such as natural image captioning, visual question answering (VQA), and spatial reasoning.
On Large Visual Language Models for Medical Imaging Analysis: An Empirical Study
Visual language models (VLMs), such as LLaVA, Flamingo, or CLIP, have demonstrated impressive performance on various visio-linguistic tasks.
Diverse Neural Audio Embeddings -- Bringing Features back !
With the advent of modern AI architectures, a shift has happened towards end-to-end architectures.
Neural Architectures Learning Fourier Transforms, Signal Processing and Much More....
Further, we can also use the convolution operation with a signal to be learned from scratch, and we will explore papers in the literature that uses this with that robust Transformer architectures.
Seasonality Based Reranking of E-commerce Autocomplete Using Natural Language Queries
Query autocomplete (QAC) also known as typeahead, suggests list of complete queries as user types prefix in the search box.
Conformer LLMs -- Convolution Augmented Large Language Models
This work aims to adapt these architectures in a causal setup for training LLMs.
Developing Speech Processing Pipelines for Police Accountability
We investigate the potential of large pre-trained speech models for facilitating reviews, focusing on ASR and officer speech detection in footage from traffic stops.
Content Adaptive Front End For Audio Signal Processing
In this work, we propose a way of computing a content-adaptive learnable time-frequency representation.
One-Shot Acoustic Matching Of Audio Signals -- Learning to Hear Music In Any Room/ Concert Hall
Typically, researchers use an excitation such as a pistol shot or balloon pop as an impulse signal with which an auralization can be created.
Generating Coherent Drum Accompaniment With Fills And Improvisations
We use the transformer sequence to sequence model to generate a basic drum pattern conditioned on the melodic accompaniment to find that improvisation is largely absent, attributed possibly to its expectedly relatively low representation in the training data.
Enhancing Audio Perception of Music By AI Picked Room Acoustics
A convolutional architecture is first trained to take in an audio sample and mimic the ratings of experts with about 78 % accuracy for various instrument families and notes for perceptual qualities.
A Language Model With Million Sample Context For Raw Audio Using Transformer Architectures
Modeling long-term dependencies for audio signals is a particularly challenging problem, as even small-time scales yield on the order of a hundred thousand samples.
Attention is All You Need? Good Embeddings with Statistics are enough:Large Scale Audio Understanding without Transformers/ Convolutions/ BERTs/ Mixers/ Attention/ RNNs or ....
A classification head (a feed-forward layer), similar to the approach in SimCLR is trained on a learned representation.
A Generative Model for Raw Audio Using Transformer Architectures
We show how causal transformer generative models can be used for raw waveform synthesis.
Audio Transformers:Transformer Architectures For Large Scale Audio Understanding. Adieu Convolutions
In addition, we also show how multi-rate signal processing ideas inspired from wavelets, can be applied to the Transformer embeddings to improve the results.
Ranked #8 on
Audio Classification
on FSD50K
Towards Human Haptic Gesture Interpretation for Robotic Systems
We demonstrate high classification accuracies among our proposed gesture definitions on a test set, emphasizing that neural net-work classifiers on the raw data outperform other combinations of feature sets and algorithms.
A Framework for Generative and Contrastive Learning of Audio Representations
In this paper, we present a framework for contrastive learning for audio representations, in a self supervised frame work without access to any ground truth labels.
Self-Supervised Learning of Context-Aware Pitch Prosody Representations
We show how contextual representations of short sung vocal lines can be implicitly learned from fundamental frequency ($F_0$) and thus be used as a meaningful feature space for downstream Music Information Retrieval (MIR) tasks.
A Deep Learning Approach for Low-Latency Packet Loss Concealment of Audio Signals in Networked Music Performance Applications
Networked Music Performance (NMP) is envisioned as a potential game changer among Internet applications: it aims at revolutionizing the traditional concept of musical interaction by enabling remote musicians to interact and perform together through a telecommunication network.
Unsupervised Learning of Audio Perception for Robotics Applications: Learning to Project Data to T-SNE/UMAP space
Daisy chaining high precision sound event detectors using signal processing combined with neural architectures and high dimensional clustering of unlabelled data is a vastly powerful idea, and can be explored in a variety of ways in future.
Ranking sentences from product description & bullets for better search
Products in an ecommerce catalog contain information-rich fields like description and bullets that can be useful to extract entities (attributes) using NER based systems.
End-to-End Spoken Language Translation
In this paper, we address the task of spoken language understanding.
Neuralogram: A Deep Neural Network Based Representation for Audio Signals
We propose the Neuralogram -- a deep neural network based representation for understanding audio signals which, as the name suggests, transforms an audio signal to a dense, compact representation based upon embeddings learned via a neural architecture.
Audio-Linguistic Embeddings for Spoken Sentences
We propose spoken sentence embeddings which capture both acoustic and linguistic content.
Conditional End-to-End Audio Transforms
We present an end-to-end method for transforming audio from one style to another.
Neural Style Transfer for Audio Spectograms
In our work, we present a method for creating new sounds using a similar approach, treating it as a style-transfer problem, starting from a random-noise input signal and iteratively using back-propagation to optimize the sound to conform to filter-outputs from a pre-trained neural architecture of interest.
Sound Multimedia Audio and Speech Processing
Detecting Institutional Dialog Acts in Police Traffic Stops
We apply computational dialog methods to police body-worn camera footage to model conversations between police officers and community members in traffic stops.
Sheet Music Reader
The goal of this project was to design an image processing algorithm that scans in sheet music and plays the music described on the page.
