Skip to content Skip to navigation

OpenStax-CNX

You are here: Home » Content » Primary Detection Methods for Laugh Tracks

Navigation

Lenses

What is a lens?

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.

This content is ...

Affiliated with (What does "Affiliated with" mean?)

This content is either by members of the organizations listed or about topics related to the organizations listed. Click each link to see a list of all content affiliated with the organization.
  • Rice University ELEC 301 Projects display tagshide tags

    This module is included inLens: Rice University ELEC 301 Project Lens
    By: Rice University ELEC 301As a part of collection: "ELEC 301 Projects Fall 2007"

    Click the "Rice University ELEC 301 Projects" link to see all content affiliated with them.

    Click the tag icon tag icon to display tags associated with this content.

Also in these lenses

  • Lens for Engineering

    This module is included inLens: Lens for Engineering
    By: Sidney BurrusAs a part of collection: "ELEC 301 Projects Fall 2007"

    Click the "Lens for Engineering" link to see all content selected in this lens.

Recently Viewed

This feature requires Javascript to be enabled.

Tags

(What is a tag?)

These tags come from the endorsement, affiliation, and other lenses that include this content.
 

Primary Detection Methods for Laugh Tracks

Module by: Keith Wilhelm. E-mail the author

Summary: This module discusses discusses the primary detection methods used in a real-time laugh track removal system. It is part of a larger series discussing the implementation of this system.

Introduction

Based on the distinct time-domain characteristics of a laugh track, it is possible to use a simple method to detect such sounds. Our primary detection method uses the envelope of the input signal to find laughs.

Finding the Envelope

Method 1

We used two methods of finding and smoothing the envelope of the input signal. In the first method, the magnitude of the input signal is fed into a low pass filter and then squared to obtain the envelope. The filter is a 1000-point fir, linear phase filter generated by MATLAB. The method is simple and easy to implement, but not very efficient.

Method 2

Another method of finding the envelope of a signal is by using the Hilbert Transform. The Hilbert Transform shifts all the positive frequencies in a signal forward by pi/2 and all the negative frequencies backward by the same amount. The envelope may then be calculated by taking the square root of the sum of the squares of the Hilbert transform and the original signal. The Hilbert Transform is calculated by taking the FFT of the input signal, multiplying the positive frequencies by j and the negative frequencies by –j, and taking the inverse FFT. As in the first method, the envelope needs to be smoothed for further processing by low-pass filtering.

Locating Laughs

Once the envelope of the signal is found, laughs are detected by a threshold system. The location routine iterates through the samples of the envelope looking for values above a given amplitude threshold. Once this threshold is reached, the routine continues, tracking how long the envelope stays above a second amplitude threshold (lower than the first). If this width reaches a given threshold, the part of the signal from where its envelope rises above the first amplitude threshold to the part where its envelope drops below the second amplitude threshold is flagged as a laugh.

Figure 1: Original signal with laughs highlighted
Figure 1 (graphics1.png)
Figure 2: Envelope of signal with amplitude thresholds
Figure 2 (graphics2.png)
Figure 3: Regions of input signal flagged as laughs
Figure 3 (graphics3.png)
Figure 4: Signal with laughs removed
Figure 4 (graphics4.png)

Results

Our primary detection method gave us good results for the sound clips we used (sitcoms with audio tracks consisting mostly of dialogue and laughs). It does however have a few shortcomings. This method has trouble detecting laughs of low volume, and tends to cut out other sounds that overlap with the laughs. Additionally, it has trouble finding precisely the beginning and end of laughs, due to variations in the envelope shapes of different laughs. We had very little trouble with false positives in the clips we tested, however more sophisticated methods would be required to distinguish between laughter and other sounds with similar time-domain characteristics (such as applause).

Content actions

Download module as:

PDF | EPUB (?)

What is an EPUB file?

EPUB is an electronic book format that can be read on a variety of mobile devices.

Downloading to a reading device

For detailed instructions on how to download this content's EPUB to your specific device, click the "(?)" link.

| More downloads ...

Add module to:

My Favorites (?)

'My Favorites' is a special kind of lens which you can use to bookmark modules and collections. 'My Favorites' can only be seen by you, and collections saved in 'My Favorites' can remember the last module you were on. You need an account to use 'My Favorites'.

| A lens I own (?)

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.

| External bookmarks