Advanced time shrinking using a drop classifier based on codec features

نویسندگان

  • Jochen Issing
  • Nikolaus Färber
  • Reinhard German
چکیده

We present an integrated approach of full-band audio time scale modification for Voice over IP communication. The concept is based on a low complexity adaptive playout method that uses frame dropping and audio concealment for time shrinking and stretching, respectively. The existing version of this method is improved using a classifier that assists in choosing which audio frames can be dropped with the least subjective impact on audio quality. To maintain low complexity, we exclusively use audio signal features that are available in the audio codec. The classification of audio frames improves audio quality of the existing method without classification by 0.5 Mean Opinion Score points while requiring significantly less computational complexity by a factor of ca 10.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

A DWT and SVM based method for rolling element bearing fault diagnosis and its comparison with Artificial Neural Networks

A classification technique using Support Vector Machine (SVM) classifier for detection of rolling element bearing fault is presented here.  The SVM was fed from features that were extracted from of vibration signals obtained from experimental setup consisting of rotating driveline that was mounted on rolling element bearings which were run in normal and with artificially faults induced conditio...

متن کامل

Objective study of the performance degradation in emotion recognition through the AMR-WB+ codec

Research in speech emotion recognition often involves features that are extracted in lab settings or scenarios where speech quality is high. However, a great deal of communication occurs through speech codecs, which alters the speech signal and features extracted from it. The purpose of this study is to report on the performance degradation in emotion recognition systems when speech is passed t...

متن کامل

Automated Detection of Multiple Sclerosis Lesions Using Texture-based Features and a Hybrid Classifier

Background: Multiple Sclerosis (MS) is the most frequent non-traumatic neurological disease capable of causing disability in young adults. Detection of MS lesions with magnetic resonance imaging (MRI) is the most common technique. However, manual interpretation of vast amounts of data is often tedious and error-prone. Furthermore, changes in lesions are often subtle and extremely unrepresentati...

متن کامل

Automatic classification of Non-alcoholic fatty liver using texture features from ultrasound images

Background: Accurate and early detection of non-alcoholic fatty liver, which is a major cause of chronic diseases is very important and is vital to prevent the complications associated with this disease. Ultrasound of the liver is the most common and widely performed method of diagnosing fatty liver. However, due to the low quality of ultrasound images, the need for an automatic and intelligent...

متن کامل

Discrimination of Power Quality Distorted Signals Based on Time-frequency Analysis and Probabilistic Neural Network

Recognition and classification of Power Quality Distorted Signals (PQDSs) in power systems is an essential duty. One of the noteworthy issues in Power Quality Analysis (PQA) is identification of distorted signals using an efficient scheme. This paper recommends a Time–Frequency Analysis (TFA), for extracting features, so-called "hybrid approach", using incorporation of Multi Resolution Analysis...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2015