Exploring Ad Effectiveness using Acoustic Features
in
Workshop: Machine Learning for Audio Signal Processing (ML4Audio)
Abstract
Online audio advertising is a form of advertising used abundantly in online music streaming services. In these platforms, providing high quality ads ensures a better user experience and results in longer user engagement. In this paper we describe a way to predict ad quality using hand-crafted, interpretable acoustic features that capture timbre, rhythm, and harmonic organization of the audio signal. We then discuss how the characteristics of the sound can be connected to concepts such as the clarity of the ad and its message.
Matt Prockup is currently a scientist at Pandora working on methods and tools for Music Information Retrieval at scale. He recently received his Ph.D. in Electrical Engineering from Drexel University. His research interests span a wide scope of topics including audio signal processing, recommender systems, machine learning, and human computer interaction. He is also an avid drummer, percussionist, and composer.
Puya - Hossein Vahabi is a senior research scientist at Pandora working on Audio/Video Computational Advertising. Before Pandora, he was a research scientist at Yahoo Labs. He has a PhD in CS, and he has been a research associate of the Italian National Research for many years. He has a PhD in CS, and his background is on computational advertising, graph mining and information retrieval.
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