Analyze emotional content and mood in audio. Identifies emotional tone, intensity, and mood shifts in voice or music for content creation or communication analysis.
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Emotion Detection is an AI-powered tool that analyzes emotional content and mood in audio recordings, identifying emotional tone, intensity, and mood shifts in both voice and music. It examines vocal characteristics, musical elements, and contextual cues to determine what emotions are being expressed and how they change throughout the audio. Unlike basic sentiment analysis that categorizes content as positive or negative, this tool provides detailed emotional analysis identifying specific emotions like joy, sadness, anger, fear, surprise, or more nuanced states like nostalgia, determination, or tranquility. It tracks how emotions evolve throughout the audio, identifies emotional intensity levels, and explains how various audio elements contribute to emotional impact. The tool analyzes both spoken content and musical elements, making it valuable for content creators assessing emotional impact, researchers studying emotional expression, performers evaluating emotional delivery, or anyone wanting to understand how audio content affects listeners emotionally.
Upload your audio file and the AI analyzes emotional content across multiple dimensions. For vocal content, it examines pitch variations, tone quality, speech rate, and vocal intensity that indicate emotional states. For musical content, it analyzes tempo, key signatures, harmonic progressions, and dynamic changes that create emotional impact. The tool identifies specific emotions present, measures emotional intensity, tracks mood changes over time, and explains how audio elements contribute to emotional expression. It examines how pitch rises with excitement or drops with sadness, how tempo speeds up with energy or slows with melancholy, how harmonic progressions create tension or resolution, and how dynamics create emotional peaks and valleys. The analysis provides a timeline of emotional changes, identifies emotional peaks and transitions, and explains the relationship between technical audio elements and emotional expression. You can specify particular emotional aspects to explore in the notes field for more focused analysis.
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