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It’s World Cup season, hurray!!!
Having to sit through 90 minutes to create a highlight video is painful (trust me, I’ve tried). But that got me thinking: What if you could automatically generate a highlight video, given the full match recording?
I booted up my laptop and put on my programming hat.
I needed a way to identify the important “highlight-worthy” moments in a game, and three approaches immediately came into mind:
Approach #1: Crowd Noise Analysis
Initially, I encountered so many problems dealing with crowd noise. What do you MEAN Barcelona fans are louder than Man City fans? Dealing with variable crowd noise baselines was a real headache.
But then, I struck gold! By calculating the RMS (Root Mean Square) crowd energy, and considering the top 10% percentile as big moments, my highlight clipper could dynamically adapt to different matches, no matter the baseline volume.
All the peaks refer to pivotal moments, which is exactly what I wanted! This approach certainly looks promising, and it identified all the key goals, big chances and fouls that happened in the first half of Barcelona vs Real Madrid 2026.
Approach #2: Whistle Detection
I really wanted this method to work, but I’m sorry to inform you that I could not get this to function reliably. There’s plenty of other noises that the crowd makes that fall in the 2-4 kHz range.
I tried plotting a spectogram, but that only left me even more confused. Let’s hope I can make sense of it by next time!
I hope you’ll follow my journey, and please drop a like if you thought this was interesting!
Open comments for this post
It’s World Cup season, hurray!!!
Having to sit through 90 minutes to create a highlight video is painful (trust me, I’ve tried). But that got me thinking: What if you could automatically generate a highlight video, given the full match recording?
I booted up my laptop and put on my programming hat.
I needed a way to identify the important “highlight-worthy” moments in a game, and three approaches immediately came into mind:
Approach #1: Crowd Noise Analysis
Initially, I encountered so many problems dealing with crowd noise. What do you MEAN Barcelona fans are louder than Man City fans? Dealing with variable crowd noise baselines was a real headache.
But then, I struck gold! By calculating the RMS (Root Mean Square) crowd energy, and considering the top 10% percentile as big moments, my highlight clipper could dynamically adapt to different matches, no matter the baseline volume.
All the peaks refer to pivotal moments, which is exactly what I wanted! This approach certainly looks promising, and it identified all the key goals, big chances and fouls that happened in the first half of Barcelona vs Real Madrid 2026.
Approach #2: Whistle Detection
I really wanted this method to work, but I’m sorry to inform you that I could not get this to function reliably. There’s plenty of other noises that the crowd makes that fall in the 2-4 kHz range.
I tried plotting a spectogram, but that only left me even more confused. Let’s hope I can make sense of it by next time!
I hope you’ll follow my journey, and please drop a like if you thought this was interesting!
