Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Masking and Demasking Agents01:19

Masking and Demasking Agents

4.0K
EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...
4.0K
¹H NMR: Complex Splitting01:13

¹H NMR: Complex Splitting

2.1K
A proton M that is coupled to a proton X results in doublet signals for M. However, NMR-active nuclei can be simultaneously coupled to more than one nonequivalent nucleus. When M is coupled to a second proton A, such as in styrene oxide, each peak in the doublet is split into another doublet.
Splitting diagrams or splitting tree diagrams are routinely used to depict such complex couplings. While drawing splitting diagrams, the splitting with the larger coupling constant is usually applied...
2.1K
¹H NMR Signal Integration: Overview00:58

¹H NMR Signal Integration: Overview

4.0K
The intensity of a signal, which can be represented by the area under the peak, depends on the number of protons contributing to that signal. The area under each peak is shown as a vertical line called an integral, with the integral value listed under it, as seen in the proton NMR spectrum of benzyl acetate. Each integral value is divided by the smallest integral value to obtain the ratio of the number of protons producing each signal. The ratio reveals the relative number of protons and not...
4.0K
¹H NMR Signal Multiplicity: Splitting Patterns01:13

¹H NMR Signal Multiplicity: Splitting Patterns

8.4K
When protons A and X are coupled, their nuclear spin energy levels are slightly modified. This is because the energy required to excite proton A to a spin state parallel to proton X is slightly different from the energy required for it to become anti-parallel to spin X. Consequently, there are two possible excitation frequencies for A (A1 and A2), depending on the spin state of X, and vice versa. The mutual nature of coupling implies that the difference between frequencies A1 and A2, indicated...
8.4K
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

8.9K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
8.9K
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

5.4K
The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an...
5.4K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A speech prediction model based on codec modeling and transformer decoding.

Computer speech & language·2026
Same author

A Molecular Trimming Strategy for Hypoxia-Tolerant Photosensitizers With Enhanced cGAS-STING Activation.

Angewandte Chemie (International ed. in English)·2026
Same author

Towards decoupling frontend enhancement and backend recognition in monaural robust ASR.

Computer speech & language·2026
Same author

Efficacy of SWIM technology combined with direct aspiration first pass technique for large vessel occlusion in acute ischemic stroke.

American journal of translational research·2026
Same author

Manipulating RTP properties of the same organic molecule by polymorphic engineering.

Chemical communications (Cambridge, England)·2025
Same author

Confined Growth of 2D Covalent Organic Framework Nanosheets with Controlled Thickness for Osmotic Energy Conversion.

Small (Weinheim an der Bergstrasse, Germany)·2025
Same journal

<math></math> Estimation and Voicing Detection With Cascade Architecture in Noisy Speech.

IEEE/ACM transactions on audio, speech, and language processing·2025
Same journal

Speech Enhancement for Cochlear Implant Recipients using Deep Complex Convolution Transformer with Frequency Transformation.

IEEE/ACM transactions on audio, speech, and language processing·2025
Same journal

Selective Acoustic Feature Enhancement for Speech Emotion Recognition With Noisy Speech.

IEEE/ACM transactions on audio, speech, and language processing·2024
Same journal

Glottal Airflow Estimation using Neck Surface Acceleration and Low-Order Kalman Smoothing.

IEEE/ACM transactions on audio, speech, and language processing·2023
Same journal

Bilateral Cochlear Implant Processing of Coding Strategies With CCi-MOBILE, an Open-Source Research Platform.

IEEE/ACM transactions on audio, speech, and language processing·2023
Same journal

Robust Vocal Quality Feature Embeddings for Dysphonic Voice Detection.

IEEE/ACM transactions on audio, speech, and language processing·2023
See all related articles

Related Experiment Video

Updated: Mar 22, 2026

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

2.1K

Complex Ratio Masking for Monaural Speech Separation.

Donald S Williamson1, Yuxuan Wang2, DeLiang Wang3

  • 1Department of Computer Science and Engineering, The Ohio State University, Columbus, OH 43210 USA ( williado@cse.ohio-state.edu ).

IEEE/ACM Transactions on Audio, Speech, and Language Processing
|April 13, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a novel deep neural network for monaural speech separation, enhancing both magnitude and phase spectra in the complex domain. The proposed method significantly improves speech separation quality and perceptual evaluation of speech quality (PESQ) scores.

Keywords:
Deep neural networkscomplex ideal ratio maskspeech qualityspeech separation

More Related Videos

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
09:09

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

Published on: September 27, 2024

964
Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.9K

Related Experiment Videos

Last Updated: Mar 22, 2026

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

2.1K
Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
09:09

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

Published on: September 27, 2024

964
Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.9K

Area of Science:

  • Signal Processing
  • Machine Learning
  • Acoustics

Background:

  • Traditional speech separation methods focus on the magnitude spectrum of the short-time Fourier transform (STFT), neglecting the phase spectrum.
  • The perceived importance of phase spectrum in speech enhancement has been historically underestimated, limiting system performance.
  • Recent research indicates phase spectrum's critical role in perceptual speech quality, necessitating its enhancement.

Purpose of the Study:

  • To develop a supervised monaural speech separation system that simultaneously enhances both magnitude and phase spectra.
  • To operate within the complex domain for a more comprehensive speech separation approach.
  • To evaluate the effectiveness of the proposed method against existing speech separation techniques.

Main Methods:

  • A deep neural network (DNN) was employed for supervised monaural speech separation.
  • The DNN estimates the real and imaginary components of the ideal ratio mask in the complex domain.
  • The approach operates directly on the complex spectrum, jointly enhancing magnitude and phase information.

Main Results:

  • The proposed complex domain approach demonstrated superior performance over related speech separation systems.
  • Objective metrics, including the Perceptual Evaluation of Speech Quality (PESQ), showed significant improvements.
  • Listening tests revealed a strong listener preference for the enhanced speech, with acceptance rates of at least 69%.

Conclusions:

  • Simultaneous enhancement of magnitude and phase spectra in the complex domain is crucial for effective monaural speech separation.
  • Deep neural networks are well-suited for estimating complex-valued ideal ratio masks.
  • The proposed method offers a significant advancement in speech separation technology, improving both objective and subjective quality.