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System Overview

Module by: Yi-Chieh Wu. E-mail the author

Summary: An overview of our Musical Instrument Recognition System.

Figure 1: System Flowchart.
Pitch and Instrument Recognition System Diagram
Pitch and Instrument Recognition System
Diagram (Graphic1)

The system takes some training songs and creates an output vector of features that characterize the signal. A Gaussian mixture model (GMM) is trained to identify patterns and predict an output instrument classification given a set of features.

Each digitized signal was windowed into smaller chunks for feature processing. In training, features were calculated for each window and concatenated into a single vector to be fed into the GMM for training. In testing, features were calculated for each window and fed into the GMM for classification. If multiple notes were to be detected, we recurred on the same window until we found the maximum number of notes or until a note could no longer be detected (as evaluated using a cutoff threshold for what constitutes silence).

From a user standpoint, the user must input a set of training songs, which includes a wav file and the instrument that produced the sound at specific times. Once the system is trained, the user can then input a new song, and our algorithm will output the song in “piano roll” format, i.e. the pitch and instrument of notes plotted over time.

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Definition of a lens

Lenses

A lens is a custom view of the content in the repository. You can think of it as a fancy kind of list that will let you see content through the eyes of organizations and people you trust.

What is in a lens?

Lens makers point to materials (modules and collections), creating a guide that includes their own comments and descriptive tags about the content.

Who can create a lens?

Any individual member, a community, or a respected organization.

What are tags? tag icon

Tags are descriptors added by lens makers to help label content, attaching a vocabulary that is meaningful in the context of the lens.

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