Cookies help us display personalized product recommendations and ensure you have great shopping experience.

By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    media monitoring
    Signals In The Noise: Using Media Monitoring To Manage Negative Publicity
    5 Min Read
    data analytics
    How Data Analytics Can Help You Construct A Financial Weather Map
    4 Min Read
    financial analytics
    Financial Analytics Shows The Hidden Cost Of Not Switching Systems
    4 Min Read
    warehouse accidents
    Data Analytics and the Future of Warehouse Safety
    10 Min Read
    stock investing and data analytics
    How Data Analytics Supports Smarter Stock Trading Strategies
    4 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: How to compare two audio files quality wise?
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Big Data > Data Mining > How to compare two audio files quality wise?
Data Mining

How to compare two audio files quality wise?

Editor SDC
Editor SDC
3 Min Read
SHARE

In order to compare two audio signals we introduce theanalytical module, which compares separately combined pairs of fragments ofactive and inactive phase signal that allows getting more accurate estimation.

For each fragment we determine integral spectrum by usingdiscrete cosine transformation (DCT). Spectrum integration is calculatedaccording to the proprietary formula. In the spectrum calculation the interpenetrationof windows comes to N/2 samples, and the Hamming or Blackmann-Harris windowfunction is applied to every window. Levels of spectrum energy on bands aredetermined for all sets of bands. Groups of critical bands, determined bydifferent authors resulting from different models of sound perception andspeech production.

Band boundaries (initial and terminal indices) as well asband energy values we determine by a set of proprietary formulas. The initialquality estimation value is taken as 100%, which decreases proportionally todistinction of energies on bands. The most interesting fact is that we canscale down our percentage of signals similarity to the well-known Mean OpinionScore (MOS) values, which correspond in tests to Cisco MOS or ITU P.862 asprecise as 97%. More intere…

More Read

Enhancing Collective Defense with Taxonomies for Operational Cyber Defense
What Big Data Has Helped Us Learn About Wall Street
Data Preparation: Know Your Records!
CRISP-DM Data Mining Methodology in Hypertext Form
Analytics and the myth of the aha moment

In order to compare two audio signals we introduce theanalytical module, which compares separately combined pairs of fragments ofactive and inactive phase signal that allows getting more accurate estimation.

For each fragment we determine integral spectrum by usingdiscrete cosine transformation (DCT). Spectrum integration is calculatedaccording to the proprietary formula. In the spectrum calculation the interpenetrationof windows comes to N/2 samples, and the Hamming or Blackmann-Harris windowfunction is applied to every window. Levels of spectrum energy on bands aredetermined for all sets of bands. Groups of critical bands, determined bydifferent authors resulting from different models of sound perception andspeech production.

Band boundaries (initial and terminal indices) as well asband energy values we determine by a set of proprietary formulas. The initialquality estimation value is taken as 100%, which decreases proportionally todistinction of energies on bands. The most interesting fact is that we canscale down our percentage of signals similarity to the well-known Mean OpinionScore (MOS) values, which correspond in tests to Cisco MOS or ITU P.862 asprecise as 97%. More interesting facts to follow – stay in touch!

Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

data science professor
The Power of Warm-Ups: Setting the Stage for Learning
Exclusive News
cloud dataops for metering
Taming the IoT Firehose: How Utilities Are Scaling Cloud DataOps for Smart Metering
Cloud Computing Exclusive Internet of Things IT
ai in video game development
Machine Learning Is Changing iGaming Software Development
Exclusive Machine Learning News
media monitoring
Signals In The Noise: Using Media Monitoring To Manage Negative Publicity
Analytics Exclusive Infographic

Stay Connected

1.2KFollowersLike
33.7KFollowersFollow
222FollowersPin

You Might also Like

Live from Strata

2 Min Read

Apple Introduces Revolutionary New Laptop With No Keyboard | The…

0 Min Read

Data Mining in the New Economy and How to Get Started

12 Min Read

Surviving the downturn lesson #73431

6 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

ai in ecommerce
Artificial Intelligence for eCommerce: A Closer Look
Artificial Intelligence
ai is improving the safety of cars
From Bolts to Bots: How AI Is Fortifying the Automotive Industry
Artificial Intelligence

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-25 SmartData Collective. All Rights Reserved.
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?