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Abdulrasaq Adebayo

Baby Cry Detection System Data Labeling

Nigeria flagN/A, Nigeria
$10.00/hrIntermediateClickworkerData Annotation TechMercor

Key Skills

Software

ClickworkerClickworker
Data Annotation TechData Annotation Tech
MercorMercor
OneFormaOneForma
RemotasksRemotasks
TolokaToloka

Top Subject Matter

Baby cry detection
Audio Processing
Speech Transcription

Top Data Types

AudioAudio
TextText
Computer Code ProgrammingComputer Code Programming

Top Task Types

ClassificationClassification
TranscriptionTranscription

Freelancer Overview

Baby Cry Detection System Data Labeling. Brings 6+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal and Proprietary Tooling. Education includes Bachelor of Science, Kwara State University. AI-training focus includes data types such as Audio and Text and labeling workflows including Classification, Transcription, and Evaluation.

IntermediateEnglish

Labeling Experience

AI Agent Training and Evaluation

Text
I developed and debugged Python code for agent training pipelines, tasks, and evaluation processes. This included evaluating model outputs, rating their quality, and providing feedback for fine-tuning. The experience enabled continuous improvement of AI agent performance in multiple domains. • Evaluated text outputs of AI agents and provided structured ratings. • Participated in iterative training and retraining cycles for agent enhancement. • Troubleshot and resolved coding issues in training pipelines. • Collaborated with cross-functional teams on data quality and evaluation documentation.

I developed and debugged Python code for agent training pipelines, tasks, and evaluation processes. This included evaluating model outputs, rating their quality, and providing feedback for fine-tuning. The experience enabled continuous improvement of AI agent performance in multiple domains. • Evaluated text outputs of AI agents and provided structured ratings. • Participated in iterative training and retraining cycles for agent enhancement. • Troubleshot and resolved coding issues in training pipelines. • Collaborated with cross-functional teams on data quality and evaluation documentation.

2025 - Present

Transcription Application Data Annotation

TextTranscription
I managed and labeled data in a transcription application using Python backend tools. This experience involved annotating and transcribing speech data to power automated transcription services. My work contributed to the development of highly accurate transcription models. • Processed and annotated large volumes of speech-to-text data. • Verified transcription accuracy and corrected errors as needed. • Integrated and utilized cloud services for scalable data processing. • Leveraged Python scripting for data cleaning and organization.

I managed and labeled data in a transcription application using Python backend tools. This experience involved annotating and transcribing speech data to power automated transcription services. My work contributed to the development of highly accurate transcription models. • Processed and annotated large volumes of speech-to-text data. • Verified transcription accuracy and corrected errors as needed. • Integrated and utilized cloud services for scalable data processing. • Leveraged Python scripting for data cleaning and organization.

Not specified

Baby Cry Detection System Data Labeling

AudioClassification
I developed a machine learning model to detect baby cries, which involved labeling and annotating audio data of infant sounds. The process included classifying different types of cries and background noises to enhance model accuracy. This work contributed to the improvement of automated baby cry recognition systems. • Collected and annotated audio samples of baby cries and ambient noises. • Classified audio segments into relevant categories for model training. • Ensured data quality by conducting rigorous validation and verification steps. • Utilized Python-based tools to preprocess and manage the audio dataset.

I developed a machine learning model to detect baby cries, which involved labeling and annotating audio data of infant sounds. The process included classifying different types of cries and background noises to enhance model accuracy. This work contributed to the improvement of automated baby cry recognition systems. • Collected and annotated audio samples of baby cries and ambient noises. • Classified audio segments into relevant categories for model training. • Ensured data quality by conducting rigorous validation and verification steps. • Utilized Python-based tools to preprocess and manage the audio dataset.

Not specified

Education

K

Kwara State University

Bachelor of Science, Mechanical Engineering

Bachelor of Science
Not specified

Work History

L

Lantop Infotech

Software Developer

N/A
2025 - Present
T

Turing

Senior Python Developer

San Francisco
2021 - 2024