NLP Signal Processing: Transcription & Sentiment Analysis
Budget: $30 – $250 CAD
I'm seeking an expert in Natural Language Processing (NLP) with a focus on signal processing. The project involves two primary tasks:
- Transcribing sound vocalizations and understanding their semantics.
- Performing multi-class sentiment analysis on the transcribed text.
The ideal candidate will have extensive experience with both speech recognition and text analysis, particularly in the realm of sentiment detection. This project will require you to categorize sentiments into positive, negative, or neutral classes.
Key Skills:
- Proficiency in NLP and signal processing
- Experience with speech recognition and text analysis
- Expertise in multi-class sentiment analysis
- Strong transcription skills
Your ability to accurately transcribe and analyze vocalized sentiments will be crucial for the success of this project.
Signal Processing and Natural Language Processing (NLP) to help analyze a substantial dataset of sound files. The dataset is 39.5 GB in size and is spread across 10 date-stamped folders. - **Primary Objective:** The main goal of this project is to classify various sounds within the dataset, which predominantly consists of animal sounds. - **Expected Deliverables:** The analysis should culminate in the creation of comprehensive visualizations that effectively represent the data and the sound classifications. Ideal candidates for this project should have: - Extensive experience in signal processing and sound classification. - Proficiency in creating clear and insightful visual representations of data. - A background or strong interest in working with animal sounds would be advantageous. Each main folder contains several subfolders numbered according to different subjects or data sources. Inside each numbered subfolder, there are two types of sound files categorized as Low Frequency (LFC) and High Frequency (HFC). File sizes vary, ranging from brief clips in kilobytes to extensive raw data files spanning several hours. Review and assess the data structure. Develop algorithms to perform semantic analysis on the sound files to derive meaningful insights. Provide the processed dataset, complete source codes used for analysis, and a detailed written report of findings and methodologies. Deliverables Algorithms and source codes. Processed dataset. Comprehensive written report. Proven experience in signal processing and NLP. Ability to handle large datasets and perform complex data analysis. Excellent problem-solving skills and creativity in translating data into actionable insights. Questions for Applicants Could you review the provided data structure and confirm your ability to perform the required semantic analysis? What would be your asking price for the complete scope of work? What is your estimated timeline for delivering the algorithms, codes, processed dataset, and detailed report?
- Transcribing sound vocalizations and understanding their semantics.
- Performing multi-class sentiment analysis on the transcribed text.
The ideal candidate will have extensive experience with both speech recognition and text analysis, particularly in the realm of sentiment detection. This project will require you to categorize sentiments into positive, negative, or neutral classes.
Key Skills:
- Proficiency in NLP and signal processing
- Experience with speech recognition and text analysis
- Expertise in multi-class sentiment analysis
- Strong transcription skills
Your ability to accurately transcribe and analyze vocalized sentiments will be crucial for the success of this project.
Signal Processing and Natural Language Processing (NLP) to help analyze a substantial dataset of sound files. The dataset is 39.5 GB in size and is spread across 10 date-stamped folders. - **Primary Objective:** The main goal of this project is to classify various sounds within the dataset, which predominantly consists of animal sounds. - **Expected Deliverables:** The analysis should culminate in the creation of comprehensive visualizations that effectively represent the data and the sound classifications. Ideal candidates for this project should have: - Extensive experience in signal processing and sound classification. - Proficiency in creating clear and insightful visual representations of data. - A background or strong interest in working with animal sounds would be advantageous. Each main folder contains several subfolders numbered according to different subjects or data sources. Inside each numbered subfolder, there are two types of sound files categorized as Low Frequency (LFC) and High Frequency (HFC). File sizes vary, ranging from brief clips in kilobytes to extensive raw data files spanning several hours. Review and assess the data structure. Develop algorithms to perform semantic analysis on the sound files to derive meaningful insights. Provide the processed dataset, complete source codes used for analysis, and a detailed written report of findings and methodologies. Deliverables Algorithms and source codes. Processed dataset. Comprehensive written report. Proven experience in signal processing and NLP. Ability to handle large datasets and perform complex data analysis. Excellent problem-solving skills and creativity in translating data into actionable insights. Questions for Applicants Could you review the provided data structure and confirm your ability to perform the required semantic analysis? What would be your asking price for the complete scope of work? What is your estimated timeline for delivering the algorithms, codes, processed dataset, and detailed report?
Related categories:
Transcription
Natural Language Processing
Whisper AI
Hugging Face
Sentiment Analysis