특징 추출
머신러닝 또는 딥러닝 시스템에 대한 입력으로 사용할 오디오 신호에서 특징을 추출합니다. 개별 함수(예: melSpectrogram, mfcc, pitch, spectralCentroid)를 사용하거나 audioFeatureExtractor 객체를 사용하여 중복 계산을 최소화하는 특징 추출 파이프라인을 만듭니다. Simulink®의 오디오 신호에서 특징을 추출하려면 Mel Spectrogram 및 MFCC와 같은 블록을 사용하십시오. 라이브 스크립트에서는 오디오 특징 추출을 사용하여 추출할 특징을 시각적으로 선택하십시오.
객체
audioFeatureExtractor | Streamline audio feature extraction |
ivectorSystem | Create i-vector system (R2021a 이후) |
라이브 편집기 작업
| 오디오 특징 추출 | 라이브 편집기에서 오디오 특징 추출 간소화 |
함수
블록
| Audio Delta | Compute delta features (R2022b 이후) |
| Auditory Spectrogram | Extract mel, Bark, or ERB spectrogram from audio (R2022a 이후) |
| Cepstral Coefficients | Extract cepstral coefficients from spectrogram (R2022b 이후) |
| Design Auditory Filter Bank | Design frequency-domain auditory filter bank (R2022a 이후) |
| Design Mel Filter Bank | Design frequency-domain mel filter bank (R2022a 이후) |
| Mel Spectrogram | Extract mel spectrogram from audio (R2022a 이후) |
| MFCC | Extract mel-frequency cepstral coefficients from audio (R2022b 이후) |
도움말 항목
- Feature Selection for Audio Classification
Perform audio feature selection to select a feature set for either speaker recognition or word recognition tasks.
- Extract Features from Audio Data Sets
Use different methods of extracting features from an audio data set.
- Spectral Descriptors
Overview and applications of spectral descriptors.
- Learn Pre-Emphasis Filter Using Deep Learning
Use a convolutional deep network to learn a pre-emphasis filter for speech recognition. (R2021b 이후)
추천 예제
Train Spoken Digit Recognition Network Using Out-of-Memory Features
Trains a spoken digit recognition network on out-of-memory auditory spectrograms using a transformed datastore. In this example, you extract auditory spectrograms from audio using audioDatastore and audioFeatureExtractor, and you write them to disk. You then use a signalDatastore to access the features during training. The workflow is useful when the training features do not fit in memory. In this workflow, you only extract features once, which speeds up your workflow if you are iterating on the deep learning model design.
Sequential Feature Selection for Audio Features
A typical workflow for feature selection applied to the task of spoken digit recognition.
Pitch Tracking Using Multiple Pitch Estimations and HMM
Perform pitch tracking using multiple pitch estimations, octave and median smoothing, and a hidden Markov model (HMM).
