Categories: FAANG

Rethinking Non-Negative Matrix Factorization with Implicit Neural Representations

This paper was accepted at the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) 2025
Non-negative Matrix Factorization (NMF) is a powerful technique for analyzing regularly-sampled data, i.e., data that can be stored in a matrix. For audio, this has led to numerous applications using time-frequency (TF) representations like the Short-Time Fourier Transform. However extending these applications to irregularly-spaced TF representations, like the Constant-Q transform, wavelets, or sinusoidal analysis models, has not been possible since these representations…

Building a Recommender System From Scratch with Matrix Factorization in Python

In this article, we will build step by step a movie recommender system in Python, based on matrix factorization.

March 29, 2025

In "AI/ML Research"

Introducing multimodal and structured data embedding support in BigQuery

April 12, 2024

In "FAANG"

Pre-trained Model Representations and their Robustness against Noise for Speech Emotion Analysis

Pre-trained model representations have demonstrated state-of-the-art performance in speech recognition, natural language processing, and other applications. Speech models, such as Bidirectional Encoder Representations from Transformers (BERT) and Hidden units BERT (HuBERT), have enabled generating lexical and acoustic representations to benefit speech recognition applications. We investigated the use of pre-trained model…

March 15, 2023

In "FAANG"

AI Generated Robotic Content