What emotion is in this voice? Upload audio to detect happiness, sadness, anger, and mood shifts in speech or music.
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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.
Upload the clip and the AI names the emotions being expressed, their intensity, and where the mood shifts. It works from the audible evidence: pitch movement, pacing, vocal energy, and tone quality. The output explains which of those cues pointed to which emotion, so you can sanity-check the reasoning yourself.
Expressed emotion leaves acoustic fingerprints. Excitement raises pitch and speeds delivery; sadness slows speech and flattens contour; anger adds intensity and hard attack; nervousness shows up in pace and instability. The AI reads those patterns and maps them to emotional labels with intensity levels, including mixed states where cues point in different directions.
No, and that distinction matters. The tool describes the emotion expressed in the audio, which is not the same as someone's inner state. People mask, perform, and suppress; a calm-sounding speaker may not be calm. Treat the output as an analysis of how the audio comes across to a listener, never as a window into someone's head.
Yes. For music it reads the emotional language of the arrangement: tempo, mode, harmonic tension and release, and dynamics. A track can get a mood arc the same way a voice memo does. For songs with vocals, the analysis covers both the performance and the production's contribution to the emotional impact.
Strong on clearly expressed, high-energy emotions (joy, anger, excitement) in clean recordings; weaker on subtle or deliberately controlled delivery, and short clips give it little to work with. Cultural and individual differences in expression add real uncertainty. Fifteen seconds or more of natural speech gets a far better read than a two-second fragment.
Yes, that's one of its main jobs. The analysis maps how the mood moves through the clip, flags transition points, and notes intensity peaks. Useful for checking whether your podcast episode, sales call, or voiceover actually builds the arc you intended rather than flatlining in the middle.
Song key finder. Upload audio and AI detects the musical key, chord progression, and modulations.
Scale and mode finder. Upload a song and AI identifies the scale, mode, and any modal interchange.
Instrument identifier. Upload audio and AI lists every instrument it hears, including layered and processed s…
Song mood analyzer. Upload music and AI describes the emotional tone, energy, and likely listener feel.
Mix quality checker. Upload your track and AI evaluates balance, stereo imaging, dynamics, and clarity.
Lyric meaning analyzer. Upload a song and AI transcribes the lyrics, then breaks down themes, symbolism, and…