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"

Learning to Detect Novel and Fine-Grained Acoustic Sequences Using Pretrained Audio Representations

This work investigates pre-trained audio representations for few shot Sound Event Detection. We specifically address the task of few shot detection of novel acoustic sequences, or sound events with semantically meaningful temporal structure, without assuming access to non-target audio. We develop procedures for pre-training suitable representations, and methods which transfer…

May 20, 2023

In "FAANG"

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