scholarly journals An auditory model of vowel normalization

1978 ◽  
Vol 63 (S1) ◽  
pp. S5-S5
Author(s):  
Steven Greenberg
Keyword(s):  
Auditory Model ◽  
Vowel Normalization

Effects of Model Performances on Music Skill Acquisition and Overnight Memory Consolidation

2014 ◽  
Vol 62 (1) ◽  
pp. 89-99 ◽  
Author(s):  
Carla D. Cash ◽  
Sarah E. Allen ◽  
Amy L. Simmons ◽  
Robert A. Duke
Keyword(s):  
Skill Acquisition ◽  
Memory Consolidation ◽  
Procedural Memory ◽  
Auditory Model ◽  
Research Participants ◽  
Nondominant Hand ◽  
The Mean ◽  
Rest Intervals

This study was designed to investigate the extent to which the presentation of an auditory model prior to learning a novel melody affects performance during active practice and the overnight consolidation of procedural memory. During evening training sessions, 32 nonpianist musicians practiced a 13-note keyboard melody with their left (nondominant) hand in twelve 30-s practice intervals separated by 30-s rest intervals. Participants were instructed to play the sequence “as quickly, accurately, and evenly as possible.” Approximately half the participants, prior to the first practice interval, listened to 10 repetitions of the target melody played at 552 tones per minute (half note = 138). All participants were tested on the target melody the following morning, approximately 12 hr after training, in three 30-s blocks separated by 30-s rest intervals. Performance was measured in terms of the mean number of correct key presses per 30-s block (CKP/B). Consistent with previous research, participants made considerable improvements in CKP/B during the evening training sessions and between the end of training and the morning test sessions. Learners who listened to the model made significantly larger gains in performance during training and between the end of training and test than did those who did not hear the model.

scholarly journals A Wavelet Auditory Model and Data Compression

1993 ◽  
Vol 1 (1) ◽  
pp. 3-28 ◽  
Author(s):  
John J. Benedetto ◽  
Anthony Teolis
Keyword(s):  
Data Compression ◽  
Auditory Model

scholarly journals “Intelligent” (statistically‐guided) algorithms for vowel normalization

1980 ◽  
Vol 68 (S1) ◽  
pp. S32-S32 ◽  
Author(s):  
Richard A. Harshman ◽  
Margaret E. Lundy ◽  
Sandra Ferrari Disner
Keyword(s):  
Vowel Normalization

Approach to vowel normalization

1975 ◽  
Vol 57 (S1) ◽  
pp. S3-S3 ◽  
Author(s):  
Hisashi Wakita
Keyword(s):  
Vowel Normalization

MODELING AUDITORY PATHWAY FOR INTELLIGENT INFORMATION ACQUISITION

2004 ◽  
Vol 01 (04) ◽  
pp. 345-356
Author(s):  
HYUNG-MIN PARK ◽  
JONG-HWAN LEE ◽  
TAESU KIM ◽  
UN-MIN BAE ◽  
BYUNG TAEK KIM ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Real World ◽  
Information Acquisition ◽  
Recognition Performance ◽  
Auditory Pathway ◽  
Blind Signal Separation ◽  
Top Down ◽  
Auditory Model ◽  
Time Frequency ◽  
Binaural Processing

An auditory model has been developed for an intelligent speech information acquisition system in real-world noisy environment. The developed mathematical model of the human auditory pathway consists of three components, i.e. the nonlinear feature extraction from cochlea to auditory cortex, the binaural processing at superior olivery complex, and the top-down attention from higher brain to the cochlea. The feature extraction is based on information-theoretic sparse coding throughout the auditory pathway. Also, the time-frequency masking is incorporated as a model of the lateral inhibition in both time and frequency domain. The binaural processing is modeled as the blind signal separation and adaptive noise canceling based on the independent component analysis with hundreds of time-delays for noisy reverberated signals. The Top-Down (TD) attention comes from familiarity and/or importance of the sensory information, i.e. the sound, and a simple but efficient TD attention model had been developed based on the error backpropagation algorithm. Also, the binaural processing and top-down attention are combined for speech signals with heavy noises. This auditory model requires extensive computing, and special hardware had been developed for real-time applications. Experimental results demonstrate much better recognition performance in real-world noisy environments.

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