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Spring/Summer 2019Professor Sarah CreelsLanguage Acquisition and Sound Recognition Lab Left to right: Ran, Amanda, Singsong, Jose, Wu, Carson, Retina, Connie, Andrew, Emily, Jean, Zoe, Felicia, KristiePreschoolers

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How to fill out sound recognition lab

01

To fill out the sound recognition lab, follow these steps:

02

Start by gathering a variety of sound samples that you want to analyze. These can be recordings of different musical instruments, nature sounds, or any other type of audio you are interested in.

03

Next, you need to choose a suitable software or programming language for sound analysis. Popular options include Python with libraries like Librosa or Essentia, MATLAB, or specialized audio processing software like Sonic Visualizer or Praat.

04

Once you have the necessary tools, you can start importing your sound samples into the chosen software or programming environment. Make sure to organize and label your samples appropriately.

05

Use the available sound analysis techniques to extract meaningful features from the sound samples. These features can include pitch, rhythm, timbre, or specific spectral characteristics.

06

Apply machine learning or pattern recognition algorithms to classify or identify the sounds based on the extracted features. This step may require training a model with labeled examples of known sounds.

07

Document your findings, including the classification accuracy or any relevant insights you discovered during the analysis.

08

Finally, present your results in a clear and organized manner, using visualizations or reports to communicate your findings effectively.

Who needs sound recognition lab?

01

Sound recognition lab can be useful for various individuals or groups, including:

02

- Researchers in the field of acoustics or audio signal processing who want to analyze and interpret sound data.

03

- Musicians or composers who are interested in studying the characteristics of different musical instruments or composing with unique sound samples.

04

- Sound engineers or producers who need to classify or categorize audio files for various projects, such as creating sound libraries or designing sound effects.

05

- Educators or students learning about sound analysis, machine learning, or pattern recognition.

06

- Companies developing voice assistants, speech recognition systems, or other applications that involve sound processing and identification.

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