/* * audio.c * * Created on: Dec 31, 2017 * Author: ondra */ #include "audio.h" #include #include #include "main.h" #include "tim.h" #include "dma.h" #include "adc.h" #include "stm32l0xx_hal.h" #include #include "arm_math.h" #include #include #include "arm_const_structs.h" #include "debug.h" #define print PRINTF extern DMA_HandleTypeDef hdma_adc; extern ADC_HandleTypeDef hadc; void ftoa(float n, char *res, int afterpoint); //static const float win_hamming_2048[] = {0.08000000f, 0.08000217f, 0.08000867f, 0.08001950f, 0.08003467f, 0.08005417f, 0.08007801f, 0.08010618f, 0.08013868f, 0.08017551f, 0.08021668f, 0.08026218f, 0.08031201f, 0.08036617f, 0.08042466f, 0.08048748f, 0.08055463f, 0.08062611f, 0.08070192f, 0.08078205f, 0.08086651f, 0.08095530f, 0.08104841f, 0.08114585f, 0.08124761f, 0.08135369f, 0.08146409f, 0.08157881f, 0.08169786f, 0.08182121f, 0.08194889f, 0.08208088f, 0.08221719f, 0.08235781f, 0.08250274f, 0.08265198f, 0.08280553f, 0.08296339f, 0.08312555f, 0.08329202f, 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0.09449391f, 0.09414437f, 0.09379903f, 0.09345789f, 0.09312096f, 0.09278824f, 0.09245973f, 0.09213544f, 0.09181537f, 0.09149952f, 0.09118790f, 0.09088050f, 0.09057734f, 0.09027841f, 0.08998372f, 0.08969327f, 0.08940706f, 0.08912510f, 0.08884738f, 0.08857392f, 0.08830470f, 0.08803975f, 0.08777905f, 0.08752261f, 0.08727044f, 0.08702253f, 0.08677889f, 0.08653952f, 0.08630442f, 0.08607359f, 0.08584704f, 0.08562478f, 0.08540679f, 0.08519308f, 0.08498366f, 0.08477853f, 0.08457769f, 0.08438113f, 0.08418887f, 0.08400090f, 0.08381724f, 0.08363786f, 0.08346279f, 0.08329202f, 0.08312555f, 0.08296339f, 0.08280553f, 0.08265198f, 0.08250274f, 0.08235781f, 0.08221719f, 0.08208088f, 0.08194889f, 0.08182121f, 0.08169786f, 0.08157881f, 0.08146409f, 0.08135369f, 0.08124761f, 0.08114585f, 0.08104841f, 0.08095530f, 0.08086651f, 0.08078205f, 0.08070192f, 0.08062611f, 0.08055463f, 0.08048748f, 0.08042466f, 0.08036617f, 0.08031201f, 0.08026218f, 0.08021668f, 0.08017551f, 0.08013868f, 0.08010618f, 0.08007801f, 0.08005417f, 0.08003467f, 0.08001950f, 0.08000867f, 0.08000217f, 0.08000000f}; static const float win_hamming_1024[] = {0.08000000f, 0.08000868f, 0.08003470f, 0.08007808f, 0.08013881f, 0.08021689f, 0.08031231f, 0.08042507f, 0.08055517f, 0.08070260f, 0.08086736f, 0.08104944f, 0.08124883f, 0.08146552f, 0.08169951f, 0.08195079f, 0.08221935f, 0.08250518f, 0.08280827f, 0.08312860f, 0.08346617f, 0.08382096f, 0.08419296f, 0.08458215f, 0.08498853f, 0.08541206f, 0.08585275f, 0.08631057f, 0.08678550f, 0.08727753f, 0.08778663f, 0.08831280f, 0.08885600f, 0.08941623f, 0.08999345f, 0.09058764f, 0.09119879f, 0.09182687f, 0.09247186f, 0.09313373f, 0.09381245f, 0.09450801f, 0.09522037f, 0.09594951f, 0.09669540f, 0.09745802f, 0.09823733f, 0.09903330f, 0.09984591f, 0.10067512f, 0.10152090f, 0.10238323f, 0.10326206f, 0.10415737f, 0.10506912f, 0.10599728f, 0.10694181f, 0.10790267f, 0.10887984f, 0.10987326f, 0.11088292f, 0.11190876f, 0.11295075f, 0.11400885f, 0.11508302f, 0.11617322f, 0.11727941f, 0.11840154f, 0.11953958f, 0.12069348f, 0.12186319f, 0.12304868f, 0.12424990f, 0.12546680f, 0.12669934f, 0.12794747f, 0.12921114f, 0.13049031f, 0.13178493f, 0.13309495f, 0.13442031f, 0.13576098f, 0.13711690f, 0.13848801f, 0.13987427f, 0.14127562f, 0.14269202f, 0.14412340f, 0.14556972f, 0.14703091f, 0.14850693f, 0.14999772f, 0.15150322f, 0.15302337f, 0.15455813f, 0.15610742f, 0.15767120f, 0.15924939f, 0.16084195f, 0.16244882f, 0.16406992f, 0.16570521f, 0.16735462f, 0.16901808f, 0.17069554f, 0.17238693f, 0.17409219f, 0.17581125f, 0.17754405f, 0.17929052f, 0.18105060f, 0.18282422f, 0.18461131f, 0.18641181f, 0.18822565f, 0.19005275f, 0.19189306f, 0.19374650f, 0.19561301f, 0.19749250f, 0.19938492f, 0.20129018f, 0.20320822f, 0.20513897f, 0.20708234f, 0.20903828f, 0.21100670f, 0.21298753f, 0.21498070f, 0.21698613f, 0.21900374f, 0.22103346f, 0.22307522f, 0.22512893f, 0.22719452f, 0.22927190f, 0.23136101f, 0.23346177f, 0.23557408f, 0.23769788f, 0.23983308f, 0.24197961f, 0.24413738f, 0.24630631f, 0.24848632f, 0.25067733f, 0.25287925f, 0.25509200f, 0.25731549f, 0.25954966f, 0.26179440f, 0.26404964f, 0.26631528f, 0.26859125f, 0.27087746f, 0.27317382f, 0.27548025f, 0.27779665f, 0.28012295f, 0.28245905f, 0.28480486f, 0.28716030f, 0.28952528f, 0.29189971f, 0.29428350f, 0.29667656f, 0.29907879f, 0.30149011f, 0.30391043f, 0.30633966f, 0.30877770f, 0.31122447f, 0.31367986f, 0.31614379f, 0.31861617f, 0.32109689f, 0.32358588f, 0.32608303f, 0.32858825f, 0.33110144f, 0.33362251f, 0.33615137f, 0.33868792f, 0.34123206f, 0.34378370f, 0.34634275f, 0.34890909f, 0.35148265f, 0.35406332f, 0.35665100f, 0.35924560f, 0.36184702f, 0.36445515f, 0.36706991f, 0.36969120f, 0.37231891f, 0.37495294f, 0.37759320f, 0.38023958f, 0.38289200f, 0.38555034f, 0.38821450f, 0.39088439f, 0.39355991f, 0.39624095f, 0.39892741f, 0.40161920f, 0.40431620f, 0.40701833f, 0.40972547f, 0.41243752f, 0.41515439f, 0.41787596f, 0.42060215f, 0.42333283f, 0.42606792f, 0.42880731f, 0.43155089f, 0.43429856f, 0.43705022f, 0.43980576f, 0.44256509f, 0.44532808f, 0.44809465f, 0.45086469f, 0.45363809f, 0.45641474f, 0.45919455f, 0.46197741f, 0.46476321f, 0.46755185f, 0.47034322f, 0.47313722f, 0.47593374f, 0.47873268f, 0.48153393f, 0.48433739f, 0.48714294f, 0.48995049f, 0.49275993f, 0.49557115f, 0.49838404f, 0.50119851f, 0.50401444f, 0.50683172f, 0.50965026f, 0.51246994f, 0.51529067f, 0.51811232f, 0.52093480f, 0.52375800f, 0.52658181f, 0.52940612f, 0.53223084f, 0.53505585f, 0.53788104f, 0.54070632f, 0.54353157f, 0.54635669f, 0.54918156f, 0.55200609f, 0.55483017f, 0.55765369f, 0.56047654f, 0.56329862f, 0.56611982f, 0.56894003f, 0.57175916f, 0.57457708f, 0.57739370f, 0.58020891f, 0.58302261f, 0.58583468f, 0.58864502f, 0.59145352f, 0.59426009f, 0.59706461f, 0.59986697f, 0.60266708f, 0.60546483f, 0.60826010f, 0.61105280f, 0.61384282f, 0.61663005f, 0.61941439f, 0.62219574f, 0.62497399f, 0.62774903f, 0.63052076f, 0.63328907f, 0.63605387f, 0.63881504f, 0.64157249f, 0.64432610f, 0.64707578f, 0.64982142f, 0.65256291f, 0.65530016f, 0.65803307f, 0.66076151f, 0.66348541f, 0.66620464f, 0.66891911f, 0.67162872f, 0.67433337f, 0.67703295f, 0.67972735f, 0.68241649f, 0.68510026f, 0.68777855f, 0.69045126f, 0.69311830f, 0.69577957f, 0.69843496f, 0.70108437f, 0.70372770f, 0.70636486f, 0.70899575f, 0.71162025f, 0.71423829f, 0.71684975f, 0.71945454f, 0.72205256f, 0.72464371f, 0.72722790f, 0.72980502f, 0.73237499f, 0.73493769f, 0.73749305f, 0.74004095f, 0.74258131f, 0.74511403f, 0.74763900f, 0.75015615f, 0.75266537f, 0.75516656f, 0.75765964f, 0.76014451f, 0.76262107f, 0.76508924f, 0.76754891f, 0.77000000f, 0.77244241f, 0.77487606f, 0.77730084f, 0.77971668f, 0.78212347f, 0.78452113f, 0.78690956f, 0.78928868f, 0.79165839f, 0.79401861f, 0.79636925f, 0.79871022f, 0.80104143f, 0.80336279f, 0.80567422f, 0.80797562f, 0.81026692f, 0.81254802f, 0.81481884f, 0.81707929f, 0.81932929f, 0.82156875f, 0.82379759f, 0.82601573f, 0.82822307f, 0.83041955f, 0.83260506f, 0.83477954f, 0.83694290f, 0.83909506f, 0.84123594f, 0.84336545f, 0.84548352f, 0.84759006f, 0.84968500f, 0.85176826f, 0.85383976f, 0.85589942f, 0.85794716f, 0.85998291f, 0.86200658f, 0.86401812f, 0.86601742f, 0.86800443f, 0.86997907f, 0.87194125f, 0.87389092f, 0.87582799f, 0.87775239f, 0.87966405f, 0.88156290f, 0.88344887f, 0.88532187f, 0.88718185f, 0.88902874f, 0.89086245f, 0.89268294f, 0.89449011f, 0.89628392f, 0.89806428f, 0.89983114f, 0.90158442f, 0.90332407f, 0.90505000f, 0.90676217f, 0.90846050f, 0.91014494f, 0.91181540f, 0.91347185f, 0.91511420f, 0.91674241f, 0.91835640f, 0.91995612f, 0.92154150f, 0.92311250f, 0.92466904f, 0.92621107f, 0.92773853f, 0.92925137f, 0.93074952f, 0.93223293f, 0.93370154f, 0.93515530f, 0.93659416f, 0.93801806f, 0.93942694f, 0.94082075f, 0.94219944f, 0.94356296f, 0.94491126f, 0.94624428f, 0.94756198f, 0.94886431f, 0.95015121f, 0.95142263f, 0.95267854f, 0.95391888f, 0.95514361f, 0.95635267f, 0.95754603f, 0.95872364f, 0.95988545f, 0.96103143f, 0.96216152f, 0.96327568f, 0.96437388f, 0.96545607f, 0.96652221f, 0.96757226f, 0.96860618f, 0.96962393f, 0.97062548f, 0.97161078f, 0.97257980f, 0.97353250f, 0.97446885f, 0.97538881f, 0.97629234f, 0.97717942f, 0.97805000f, 0.97890406f, 0.97974156f, 0.98056247f, 0.98136677f, 0.98215441f, 0.98292538f, 0.98367963f, 0.98441715f, 0.98513791f, 0.98584187f, 0.98652902f, 0.98719932f, 0.98785275f, 0.98848928f, 0.98910890f, 0.98971157f, 0.99029729f, 0.99086601f, 0.99141773f, 0.99195241f, 0.99247005f, 0.99297062f, 0.99345411f, 0.99392048f, 0.99436974f, 0.99480185f, 0.99521681f, 0.99561459f, 0.99599519f, 0.99635858f, 0.99670477f, 0.99703372f, 0.99734543f, 0.99763989f, 0.99791708f, 0.99817701f, 0.99841964f, 0.99864499f, 0.99885303f, 0.99904377f, 0.99921718f, 0.99937328f, 0.99951204f, 0.99963347f, 0.99973757f, 0.99982432f, 0.99989372f, 0.99994577f, 0.99998048f, 0.99999783f, 0.99999783f, 0.99998048f, 0.99994577f, 0.99989372f, 0.99982432f, 0.99973757f, 0.99963347f, 0.99951204f, 0.99937328f, 0.99921718f, 0.99904377f, 0.99885303f, 0.99864499f, 0.99841964f, 0.99817701f, 0.99791708f, 0.99763989f, 0.99734543f, 0.99703372f, 0.99670477f, 0.99635858f, 0.99599519f, 0.99561459f, 0.99521681f, 0.99480185f, 0.99436974f, 0.99392048f, 0.99345411f, 0.99297062f, 0.99247005f, 0.99195241f, 0.99141773f, 0.99086601f, 0.99029729f, 0.98971157f, 0.98910890f, 0.98848928f, 0.98785275f, 0.98719932f, 0.98652902f, 0.98584187f, 0.98513791f, 0.98441715f, 0.98367963f, 0.98292538f, 0.98215441f, 0.98136677f, 0.98056247f, 0.97974156f, 0.97890406f, 0.97805000f, 0.97717942f, 0.97629234f, 0.97538881f, 0.97446885f, 0.97353250f, 0.97257980f, 0.97161078f, 0.97062548f, 0.96962393f, 0.96860618f, 0.96757226f, 0.96652221f, 0.96545607f, 0.96437388f, 0.96327568f, 0.96216152f, 0.96103143f, 0.95988545f, 0.95872364f, 0.95754603f, 0.95635267f, 0.95514361f, 0.95391888f, 0.95267854f, 0.95142263f, 0.95015121f, 0.94886431f, 0.94756198f, 0.94624428f, 0.94491126f, 0.94356296f, 0.94219944f, 0.94082075f, 0.93942694f, 0.93801806f, 0.93659416f, 0.93515530f, 0.93370154f, 0.93223293f, 0.93074952f, 0.92925137f, 0.92773853f, 0.92621107f, 0.92466904f, 0.92311250f, 0.92154150f, 0.91995612f, 0.91835640f, 0.91674241f, 0.91511420f, 0.91347185f, 0.91181540f, 0.91014494f, 0.90846050f, 0.90676217f, 0.90505000f, 0.90332407f, 0.90158442f, 0.89983114f, 0.89806428f, 0.89628392f, 0.89449011f, 0.89268294f, 0.89086245f, 0.88902874f, 0.88718185f, 0.88532187f, 0.88344887f, 0.88156290f, 0.87966405f, 0.87775239f, 0.87582799f, 0.87389092f, 0.87194125f, 0.86997907f, 0.86800443f, 0.86601742f, 0.86401812f, 0.86200658f, 0.85998291f, 0.85794716f, 0.85589942f, 0.85383976f, 0.85176826f, 0.84968500f, 0.84759006f, 0.84548352f, 0.84336545f, 0.84123594f, 0.83909506f, 0.83694290f, 0.83477954f, 0.83260506f, 0.83041955f, 0.82822307f, 0.82601573f, 0.82379759f, 0.82156875f, 0.81932929f, 0.81707929f, 0.81481884f, 0.81254802f, 0.81026692f, 0.80797562f, 0.80567422f, 0.80336279f, 0.80104143f, 0.79871022f, 0.79636925f, 0.79401861f, 0.79165839f, 0.78928868f, 0.78690956f, 0.78452113f, 0.78212347f, 0.77971668f, 0.77730084f, 0.77487606f, 0.77244241f, 0.77000000f, 0.76754891f, 0.76508924f, 0.76262107f, 0.76014451f, 0.75765964f, 0.75516656f, 0.75266537f, 0.75015615f, 0.74763900f, 0.74511403f, 0.74258131f, 0.74004095f, 0.73749305f, 0.73493769f, 0.73237499f, 0.72980502f, 0.72722790f, 0.72464371f, 0.72205256f, 0.71945454f, 0.71684975f, 0.71423829f, 0.71162025f, 0.70899575f, 0.70636486f, 0.70372770f, 0.70108437f, 0.69843496f, 0.69577957f, 0.69311830f, 0.69045126f, 0.68777855f, 0.68510026f, 0.68241649f, 0.67972735f, 0.67703295f, 0.67433337f, 0.67162872f, 0.66891911f, 0.66620464f, 0.66348541f, 0.66076151f, 0.65803307f, 0.65530016f, 0.65256291f, 0.64982142f, 0.64707578f, 0.64432610f, 0.64157249f, 0.63881504f, 0.63605387f, 0.63328907f, 0.63052076f, 0.62774903f, 0.62497399f, 0.62219574f, 0.61941439f, 0.61663005f, 0.61384282f, 0.61105280f, 0.60826010f, 0.60546483f, 0.60266708f, 0.59986697f, 0.59706461f, 0.59426009f, 0.59145352f, 0.58864502f, 0.58583468f, 0.58302261f, 0.58020891f, 0.57739370f, 0.57457708f, 0.57175916f, 0.56894003f, 0.56611982f, 0.56329862f, 0.56047654f, 0.55765369f, 0.55483017f, 0.55200609f, 0.54918156f, 0.54635669f, 0.54353157f, 0.54070632f, 0.53788104f, 0.53505585f, 0.53223084f, 0.52940612f, 0.52658181f, 0.52375800f, 0.52093480f, 0.51811232f, 0.51529067f, 0.51246994f, 0.50965026f, 0.50683172f, 0.50401444f, 0.50119851f, 0.49838404f, 0.49557115f, 0.49275993f, 0.48995049f, 0.48714294f, 0.48433739f, 0.48153393f, 0.47873268f, 0.47593374f, 0.47313722f, 0.47034322f, 0.46755185f, 0.46476321f, 0.46197741f, 0.45919455f, 0.45641474f, 0.45363809f, 0.45086469f, 0.44809465f, 0.44532808f, 0.44256509f, 0.43980576f, 0.43705022f, 0.43429856f, 0.43155089f, 0.42880731f, 0.42606792f, 0.42333283f, 0.42060215f, 0.41787596f, 0.41515439f, 0.41243752f, 0.40972547f, 0.40701833f, 0.40431620f, 0.40161920f, 0.39892741f, 0.39624095f, 0.39355991f, 0.39088439f, 0.38821450f, 0.38555034f, 0.38289200f, 0.38023958f, 0.37759320f, 0.37495294f, 0.37231891f, 0.36969120f, 0.36706991f, 0.36445515f, 0.36184702f, 0.35924560f, 0.35665100f, 0.35406332f, 0.35148265f, 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0.08885600f, 0.08831280f, 0.08778663f, 0.08727753f, 0.08678550f, 0.08631057f, 0.08585275f, 0.08541206f, 0.08498853f, 0.08458215f, 0.08419296f, 0.08382096f, 0.08346617f, 0.08312860f, 0.08280827f, 0.08250518f, 0.08221935f, 0.08195079f, 0.08169951f, 0.08146552f, 0.08124883f, 0.08104944f, 0.08086736f, 0.08070260f, 0.08055517f, 0.08042507f, 0.08031231f, 0.08021689f, 0.08013881f, 0.08007808f, 0.08003470f, 0.08000868f, 0.08000000f}; #define NUM_SAMPLES 1024 #define BIN_SIZE (44444.444f / (NUM_SAMPLES/2.0f)) #define PK_MINDIST 1.5f #define PK_MAXFREQ 20000.0f volatile bool dma_done = false; void done_cb(DMA_HandleTypeDef*dma) { dma_done = true; } void graph(uint32_t width, uint32_t height, float *values, uint32_t count) { float max = FLT_MIN; float min = FLT_MAX; for (int i = 0; i < count; i++) { float f = values[i]; if (f < min) min = f; if (f > max) max = f; } int xstep = count / width; float ystep = 1; //(float)(max - min) / (float)height; // this somehow doenst work now char buf[100]; sprintf(buf, "min %d max %d, ystep %d, h %d\r\n", (int)min*1000, (int)max*1000, (int)ystep*1000, height); print(buf); for (int i = height-1; i >= 0; i--) { float thr = i*ystep; for (int j = 0; j < width; j++) { float acu = 0; int cnt = 0; for (int k = j*xstep; k < (j+1)*xstep && k < count; k++, cnt++) { acu += values[k]; } acu /= (float)cnt; float sample = acu;//values[j*xstep]; if (sample >= thr) { print("#"); } else { print(" "); } } print("\r\n"); } } /** * Rolling average centered at a bin * * @param arr - array of bins * @param count - number of bins * @param pos - position we're intersted in * @param len - size of the rolling window, centered around the position * @return average within the window, excluding bin at position 'pos' */ static float ravg(float *arr, uint32_t count, uint32_t pos, uint32_t len) { // XXX this needs some adjustments, it's not perfectly centered // if we're at the end or beginning, use only bins we have available uint32_t from = (pos > len/2 ? pos-len/2 : 0); uint32_t to = (pos < count-len/2 ? pos+len/2 : count-1); float acu = 0; for (uint32_t i = from; i <= to; i++) { if (i == pos) continue; acu += arr[i]; } acu /= (to - from); // not +1 because we skip the middle return acu; } /** * Quadratic interpolation to find the real peak position and magnitude * * @param pk - peak struct to store the results in * @param values - the bins array * @param vcount - size of the bins array * @param pos - position of the peak we're triyng to analyze */ static void qinterp(struct peak *pk, const float *values, uint32_t vcount, uint32_t pos) { float a = (pos>0?values[pos-1]:values[pos]); float b = values[pos]; float c = (pos0.5 || p<-0.5) p=0; pk->position = pos + p; pk->magnitude = b - 0.25 * (a - c) * p; } /** * Detect peaks in a real float spectrum * * @param peaks - destination for the peak detect algorithm, peaks are sorted from the most important * @param pcount - number of peaks to detect * @param values - the spectrum as an array of bin magnitudes * @param vcount - number of bins in the spectrum * @return average level (excluding the peaks) */ float pkdetect(struct peak *peaks, uint32_t pcount, float *values, uint32_t vcount) { uint32_t used_peaks = 0; // clear the table for (uint32_t i = 0; i < pcount; i++) { peaks[i].position = peaks[i].magnitude = peaks[i].weight = 0; } struct peak pk = {0}; // scratch peak float prev = 0; float sum = 0; for (uint32_t i = 0; i < vcount; i++) { float base = ravg(values, vcount, i, 32); float raw = values[i]; float normed = raw / base; if (i > 0) { // difference from the previous bin (this serves as the primary peak detection factor) float diff = normed - prev; if (diff > 0) { // find the precise position and magnitude qinterp(&pk, values, vcount, i); // weight for sorting the peaks pk.weight = diff * pk.magnitude; // !!!! This constant must be adjusted if bin is resized if (pk.position >= (PK_MAXFREQ/BIN_SIZE)) { // too high, discard goto pk_done; } // try to fit it in the peak list { // first we look if there's one close enough to overwrite it or use instead for (uint32_t j = 0; j < pcount; j++) { if ((pk.position > peaks[j].position - PK_MINDIST) && (pk.position < peaks[j].position + PK_MINDIST)) { // this number determines the min distance of peaks in bin units // replace if we have better weight if (peaks[j].weight < pk.weight) { peaks[j] = pk; } // else discard it goto pk_done; // exit the for and skip the following for loop } } // look for a place for this new peak, shift what is behind it for (uint32_t j = 0; j < pcount; j++) { // peaks are sorted by weight if (pk.weight > peaks[j].weight) { // shift the tail to make room if (used_peaks > 0) { for (uint32_t k = used_peaks; k > j; k--) { peaks[k] = peaks[k-1]; } } peaks[j] = pk; if (used_peaks < pcount) used_peaks++; // increment the counter if the list wasn't full yet and grew break; } } } pk_done:; } } prev = normed; sum += raw; // this is rms } // now remove some area around the found peaks (NOTE: this will cause double removal if two peaks happened to be very close together) float pksum = 0; const uint32_t pkexpand = 4; for (uint32_t i = 0; i < pcount; i++) { uint32_t pos = (uint32_t)roundf(peaks[i].position); uint32_t from = (pos > pkexpand/2 ? pos-pkexpand/2 : 0); uint32_t to = (pos < vcount-pkexpand/2 ? pos+pkexpand/2 : vcount-1); for (uint32_t j = from; j <= to; j++) { pksum += values[j]; } } float noise = (sum - pksum); if (noise < 0) noise = 0; noise /= vcount; // sort the peaks by magnitude (they are now sorted by weight, which is useful for detecting prominence but confusing for practical use later) for (uint32_t i = 0; i < pcount-1; i++) { float bestmag = peaks[i].magnitude; uint32_t bmpos = i; for (uint32_t j = i+1; j < pcount; j++) { if (peaks[j].magnitude > bestmag) { bestmag = peaks[j].magnitude; bmpos = j; } } if (bmpos != i) { // we found a better peak in the tail, let's swap pk = peaks[i]; peaks[i] = peaks[bmpos]; peaks[bmpos] = pk; } } return noise; } union { uint16_t raw[NUM_SAMPLES]; float flt[NUM_SAMPLES*2]; } samples; void audio_capture(struct peak *peaks, uint32_t pcount, float *noise, float *totalpower) { char buf[100]; print("AUDIO SAMPLING ...\r\n"); print("Starting capture...\r\n"); HAL_StatusTypeDef rv = HAL_ADC_Start_DMA(&hadc, (void*)samples.raw, (uint32_t)NUM_SAMPLES); assert_param(rv == HAL_OK); //__enable_irq(); hdma_adc.XferCpltCallback = done_cb; dma_done = false; rv = HAL_TIM_Base_Start(&htim2); // kick it off assert_param(rv == HAL_OK); print("Capture stared.\r\n"); // wait ... while(!dma_done) { HAL_Delay(100); print("waiting... "); } print("\r\nCapture completed.\r\n"); // basic scaling for (int i=NUM_SAMPLES-1; i >= 0; i--) { samples.flt[i] = (float)samples.raw[i]/2048.0f; } // remove DC offset float mean; arm_mean_f32(samples.flt, NUM_SAMPLES, &mean); for (int i = 0; i < NUM_SAMPLES; i++) { samples.flt[i] -= mean; } // zero out the second half (this is needed because otherwise the fft is not padded by zeros and doesn't work right) for (int i=NUM_SAMPLES; i < NUM_SAMPLES*2; i++) { samples.flt[i] = 0; } uint32_t bin_count = NUM_SAMPLES/2; uint32_t samp_count = NUM_SAMPLES; for (int i = samp_count - 1; i >= 0; i--) { samples.flt[i * 2 + 1] = 0; // imaginary samples.flt[i * 2] = samples.flt[i] * win_hamming_1024[i]; // real } print("FFT...\r\n"); arm_cfft_f32(&arm_cfft_sR_f32_len1024, samples.flt, 0, true); // bit reversed FFT print("Magnitude...\r\n"); arm_cmplx_mag_f32(samples.flt, samples.flt, bin_count); // get magnitude (extract real values) // normalize print("Scaling...\r\n"); arm_scale_f32(samples.flt, (1.0f/bin_count)*1000.0f, samples.flt, bin_count); // now we have the spectrum computed in samples.flt //graph(128, 22, samples.flt, bin_count/8); // doesnt work for some reason // Find the peaks... float sum = 0; for(uint32_t i = 0; i < bin_count; i++) { sum += samples.flt[i]; } *totalpower = sum; *noise = pkdetect(peaks, pcount, samples.flt, bin_count); for (int i = 0; i < pcount; i++) { sprintf(buf, "pk %d at %d Hz, mag ", i+1, (int)roundf((peaks[i].position) * BIN_SIZE)); print(buf); ftoa(peaks[i].magnitude, buf, 6); print(buf); print("\r\n"); } ftoa(*noise, buf, 6); print("Mean noise per bin = "); print(buf); print("\r\n"); } // ftoa stuff --------------- // reverses a string 'str' of length 'len' void reverse(char *str, int len) { int i=0, j=len-1, temp; while (i