The Lantern Vol. 23, No. 3, May 1955

• Les Assassins • Golf • The Dance • Philosophy for the Beginner • Spelling - Why Bother • The Hooded Paperweight • The Wonderful Gizmo • The Accident • What Happened • Old Dog Tilts Her Head • Interlude • The Monastery Mouse • Study in Rhime Royalhttps://digitalcommons.ursinus.edu/lantern/1066/thumbnail.jp

Object-Based Spatial Audio: Concept, Advantages, and Challenges

This book describes recent innovations in 3D media and technologies, with coverage of 3D media capturing, processing, encoding, and adaptation, networking aspects for 3D Media, and quality of user experience (QoE)

A Supervised Classification Algorithm for Note Onset Detection

This paper presents a novel approach to detecting onsets in music audio files. We use a supervised learning algorithm to classify spectrogram frames extracted from digital audio as being onsets or nononsets. Frames classified as onsets are then treated with a simple peak-picking algorithm based on a moving average. We present two versions of this approach. The first version uses a single neural network classifier. The second version combines the predictions of several networks trained using different hyperparameters. We describe the details of the algorithm and summarize the performance of both variants on several datasets. We also examine our choice of hyperparameters by describing results of cross-validation experiments done on a custom dataset. We conclude that a supervised learning approach to note onset detection performs well and warrants further investigation