Locating singing voice segments within music signals
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Locating Singing Voice Segments Within Music Signals. Adam Berenzweig and Daniel P.W. Ellis LabROSA, Columbia University [email protected], [email protected] LabROSA. What Where Who Why you love us. The Future as We Hear It. Online Digital Music Libraries

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Locating singing voice segments within music signals l.jpg

Locating Singing Voice Segments Within Music Signals

Adam Berenzweig and Daniel P.W. Ellis

LabROSA, Columbia University

[email protected], [email protected]


Labrosa l.jpg
LabROSA

  • What

  • Where

  • Who

  • Why you love us


The future as we hear it l.jpg
The Future as We Hear It

  • Online Digital Music Libraries

  • The Coming Age of Streaming Music Services

  • Information Retrieval: How do we find what we want?

  • Recommendation: How do we know what we want to find?

    • Collaborative Filtering vs. Content-Based

    • What is Quality?


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Motivation

  • Lyrics Recognition: Baby Steps

    • Segmentation

    • Forced Alignment

    • A Corpus

  • Song structure through singing structure?

    • Fingerprinting

    • Retreival

    • Feature for similarity measures


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Lyrics Recognition: Can YOU do it?

  • Notoriously hard, even for humans.

    • amIright.com, kissThisGuy.com

  • Why so hard?

    • Noise, music, whatever.

    • Singing is not speech: voice transformations

    • Strange word sequences (“poetry”)

  • Need a corpus


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History of the Problem

  • Segmentation for Speech Recognition: Music/Speech

    • Scheirer & Slaney

  • Forced Alignment - Karaoke

    • Cano et al. [REF NEEDED]

  • Acoustic feature design: Custom job or Kitchen Sink?

  • Idea! Use a speech recognizer: PPF (Posterior Probability Features)

    • Williams & Ellis

  • Ultimately: Source separation, CASA



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Architecture Overview

  • Entropy H

  • H/h#

  • Dynamism D

  • P(h#)

cepstra

posteriogram

Audio

PLP

Speech

Recognizer

(Neural Net)

Feature

Calculation

Time-

averaging

Segmentation

(HMM)

Gaussian

Model

Gaussian

Model


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Architecture Overview

cepstra

posteriogram

Audio

PLP

Speech

Recognizer

(Neural Net)

Neural

Net

Segmentation

(HMM)

Neural

Net


So how s that working out for you being clever l.jpg
“So how’s that working out for you, being clever?”

  • Entropy

  • Entropy excluding background

  • Dynamism

  • Background probability

  • Distribution Match: Likelihoods under single Gaussian model

    • Cepstra

    • PPF


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Recovering context with the HMM

  • Transition probabilities

    • Inverse average segment duration

  • Emission probabilities

    • Gaussian fit to time-averaged distribution

  • Segmentation: the Viterbi path

  • Evaluation

    • Frame error rate (no boundary consideration)


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Results

  • [Table, figures]

  • Listen!

    • Good, bad

    • trigger & stick

    • genre effects?



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‘m’,’n’

‘uw’

‘ey’

  • E = .61

  • Strong phones trigger, but can’t hold it

  • Production quality effect?


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‘s’

  • E = .25

  • “Trigger and Stick”


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‘bcl’,’dcl’,’b’, ‘d’

‘l’,’r’

  • E = .54

  • False phones


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Discussion

  • The Moral of the Story: Just give it the data

  • PPF is better than cepstra. Speech Recognizer is pretty powerful.

  • Why does the extra Gaussian model help PPF but not cepstra?

  • Time averaging helps PPF: proves that it’s using the overall distribution, not short-time detail (at least, when modelled by single gaussians)


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