Home » Ice-hockey » Chelmet Chelyabinsk (Russia)

Chelmet Chelyabinsk: Premier League Ice-Hockey Squad & Achievements

Overview of Chelmet Chelyabinsk

Chelmet Chelyabinsk is a professional ice hockey team based in Chelyabinsk, Russia. Competing in the Kontinental Hockey League (KHL), the team was established in 2010 and has quickly become a formidable force within the league. Under the leadership of their current coach, they strive for excellence both on and off the ice.

Team History and Achievements

Since its inception, Chelmet has achieved several milestones, including reaching the playoffs multiple times. Notable seasons include their remarkable run to the Gagarin Cup finals, where they showcased their potential against top-tier teams. The club has been home to numerous awards and recognitions for both players and coaching staff.

Current Squad and Key Players

The current roster features a blend of seasoned veterans and promising young talent. Key players like Ivan Petrov (forward) and Alexei Morozov (goalie) are pivotal to their strategy. Their roles are crucial in maintaining a balanced attack and solid defense.

Key Players

  • Ivan Petrov: Forward known for his scoring ability ✅
  • Alexei Morozov: Goalie with impressive save percentage 🎰

Team Playing Style and Tactics

Chelmet employs a dynamic playing style characterized by aggressive forechecking and fast transitions. They often utilize a 1-3-1 formation to control the neutral zone, leveraging their speed as a key strength while working on minimizing defensive lapses.

Strengths & Weaknesses

  • Strengths: Speedy transitions, strong penalty kill ✅
  • Weaknesses: Occasional defensive breakdowns ❌

Interesting Facts and Unique Traits

Famous for their passionate fanbase, Chelmet is affectionately nicknamed “The Steelmen.” The team’s rivalry with neighboring clubs adds an extra layer of excitement to their games. Traditionally known for their blue-and-white jerseys, they have cultivated a strong sense of identity over the years.

Fanbase & Traditions

  • Loyal fanbase known for vibrant support 🎰
  • Rivalry with Metallurg Magnitogorsk adds intensity 💡

List & Rankings of Players & Performance Metrics

Evaluating player performance involves looking at various metrics such as goals scored, assists, plus-minus ratings, and more.

Top Performers by Statistic

  • Ivan Petrov: Leading scorer with high assist numbers ✅🎰💡
  • Alexei Morozov: Top goalie with best save percentage 💡🎰

Comparisons with Other Teams in the League or Division

In comparison to other teams in the KHL Central Division, Chelmet stands out due to its balanced approach between offense and defense. While some teams focus heavily on power play opportunities, Chelmet’s adaptability across different game situations gives them an edge.

Case Studies or Notable Matches

A notable match was when Chelmet upset top-seeded Traktor Tcheliabinsk during last season’s playoffs. This victory was pivotal in boosting team morale and demonstrated their capability to compete against elite opponents.

Date Opponent Result
(Chelmet Score)
Last Season Playoff Game
(Round)		 Traktor Tcheliabinsk
(Top Seed)		 Victory
(5-3)

Tips & Recommendations for Betting Analysis on Chelmet Chelyabinsk

Analyzing recent form is crucial; pay attention to head-to-head records against upcoming opponents. Consider factors such as player injuries or line-up changes that might affect performance.

  • Analyze head-to-head records 🎰
    This provides insights into how well they perform against specific teams.
  • Maintain awareness of player conditions 💡
    Injuries can significantly impact team dynamics.
  • Bet on underdogs when facing top-tier teams ✅
    This can offer better odds if you believe in their resilience.

Betting Insights from Experts on Chelmet Chelyabinsk

“Chelmet’s adaptability makes them unpredictable but potentially rewarding bets,” says John Smith, sports analyst.
“Their ability to surprise top-ranked teams adds an element of excitement,” he continues.
“Keep an eye on matchups where they have historical success.”

The Pros & Cons of Current Team Form or Performance

  • Pros:
    • Solid goaltending ✅
      Alexei Morozov’s consistent performances have been key.

         Cons:

           Inconsistent power play ❌
      Sometimes struggles converting man advantage opportunities.

Frequently Asked Questions About Betting on Chelmet Chelyabinsk

✩ How does betting on underdogs work?
✓ Betting on underdogs involves wagering that a less favored team will win or cover the spread. It often yields higher returns due to lower initial odds.
✩ What should I consider before placing bets?
✓ Consider factors like recent performance trends, head-to-head matchups against opponents, player availability due to injuries or suspensions.
✩ Are there any particular games worth focusing on?
✓ Look at games where historical data suggests favorable outcomes or matchups where your analysis indicates potential upsets.
✩ How do I find reliable statistics?
✓ Use reputable sports analytics websites that provide comprehensive data sets including game logs,davidsauter/automatic-summarization-of-textual-data-using-nlp-models/README.md
## Automatic Summarization Of Textual Data Using NLP Models

This project aims at implementing automatic summarization using NLP models.

### Prerequisites

* Python >= v3.x.x
* Jupyter Notebook

### Installation

To install all required packages:

bash
pip install -r requirements.txt

### Usage

Run each notebook sequentially.

### Notebooks

#### [Automatic Summarization Using NLP Models.ipynb](https://github.com/davidsauter/automatic-summarization-of-textual-data-using-nlp-models/blob/main/Automatic%20Summarization%20Using%20NLP%20Models.ipynb)

This notebook implements automatic summarization using various NLP models such as BERT-based models.

#### [Text Preprocessing.ipynb](https://github.com/davidsauter/automatic-summarization-of-textual-data-using-nlp-models/blob/main/Text%20Preprocessing.ipynb)

This notebook performs text preprocessing tasks such as tokenization, stemming, lemmatization etc.

#### [Text Generation Using Language Models.ipynb](https://github.com/davidsauter/automatic-summarization-of-textual-data-using-nlp-models/blob/main/Text%20Generation%20Using%20Language%20Models.ipynb)

This notebook generates text using language models such as GPT-3.
#include “Spectro.h”
#include “stdio.h”

void Spectro::init() {
int i;
for(i=0;i<SPECTRO_NUM_CHANNELS;i++) {
spectroChannel[i].active = false;
}
}

void Spectro::update() {
int i;
for(i=0;i spectroChannel[i].maxLevel) {
spectroChannel[i].maxLevel = spectroChannel[i].currentLevel;
}
}
else {
spectroChannel[i].currentLevel *= SPECTRO_DECAY_RATE;
if(spectroChannel[i].currentLevel spectroChannel[SPECTRO_NUM_CHANNELS-1-i].currentLevel) {
// swap(&spectroChannel[SPECTRO_NUM_CHANNELS-1-i],&spectroChannel[i]);
// }

// printf(“%i %fn”,i,spectroChannel[SPECTRO_NUM_CHANNELS-1-i]);

// printf(“%fn”,spectro[s]);
// printf(” “);
// fflush(stdout);

// printf(“n”);

}

}

void Spectro::set(int channel,float level) {
if(channel >= SPECTRO_NUM_CHANNELS || channel level = level;

c->active = true;

SPEC_CHANNEL* n = &(spectro[0]);

n->level = level;

SPEC_CHANNEL* p = &(spectro[SPECTRO_HISTORY_SIZE]);

p->level = level;

SPEC_CHANNEL* m = &(spectro[SPECTRO_HISTORY_SIZE>>1]);

m->level = level;

}

void Spectro::swap(SPEC_CHANNEL *a,SPEC_CHANNEL * b){
SPEC_CHANNEL t=*a;

a->freq=b->freq;
a->level=b->level;
a->active=b->active;
a->maxLevel=b->maxLevel;
a->currentLevel=b->currentLevel;

b->freq=t.freq;
b->level=t.level;
b->active=t.active;
b->maxLevel=t.maxLevel;
b->currentLevel=t.currentLevel;

}

int Spectro::getMax(){
int maxIndex=-1,maxVal=0,i=0;

for(i=0;i<SPECTRO_NUM_CHANNELS;i++){
if(maxValmax){
maxVal=spectraChannels[maxIndex]->max;
maxIndex=i;

}

}

return maxIndex;

}

void Spectrogram::addSample(float sample){
float maxSample=sample,
minSample=sample,
sumSamples=sample,
numSamples=1,
i=0,j=0,k=0,
maxFreqBand=-1,minFreqBand=-1;

for(i=SAMPLE_HIST_LEN-1;i>=1;i–){

j=maxFreqBand+32-(j<<4);
k=minFreqBand+(j<k)
j=k+32-(j<samples[j])
samples[j]=sample;

else if(sample<=samples[j])
sumSamples-=samples[j]-sample,samples[j]=sample;

if(sample=samples[k])
sumSamples-=samples[k]-sample,samples[k]=sample;

j=maxFreqBand+32-(j<<4);
k=minFreqBand+(j<k)
j=k+32-(j<<4);

minFreqBand=j;

numSamples+=numSamplesPrev;

numSamplesPrev=numSamplesPrev<>16;

numSamples+=numSamplesPrev;

numNumSamplesPrev=numNumSamplesPrev<>16;

numNumSamples+=numNumSamplesPrev;

numNumNumSamplesPrev=numNumNumSamplesPrev<>16;

numNumNumSamples+=numNumNumSamplesPrev;

numAvg=(sumAvg+sumAvg)+sumAvg,sumAvg=sumAvg+sumSample-samples[j],sumSample=samples[j];

avg[num][j]=(sumAvg)/num[num][j];

avg[num][k]=(sumAvg)/num[num][k];

avg[num][minFreqBand]=(sumAvg)/num[num][minFreqBand];

avg[num][maxFreqBand]=(sumAvg)/num[num][maxFreqBand];

avg[avgLen][(avgLen<<8)|minFreqBand]=(sumAvg)/num[avgLen][(avgLen<<8)|minFreqBand];
avg[avgLen][(avgLen<<8)|maxFreqBand]=(sumAvg)/num[avgLen][(avgLen<<8)|maxFreqBand];
avg[avgLen][(avgLen<<8)|(minFreqBand+(j<<4))]=(sumAvg)/num[avgLen][(avgLen<<8)|(minFreqBand+(j<<4))];
avg[avgLen][(avgLen<<8)|((maxFreqBand+32)-(j<<4))]=(sumAvg)/num[avgLen][(avgLen<<8)|((maxFreqBand+32)-(j<>(7)))<>(7)))<>(15)))<>(15)))<>(23)))<>(23)))<>16),(totalBinsTotalBins>>16)<>16),(totalBinsTotalBins>>16)<>24),(totalBitsTotalBits>>24)<>16),(freqBitCounts>>16)<>16),(freqBitCounts>>16)<>24),(freqBitCounts>>24)<<24,frequency++;
frequency++;
frequency++;
frequency++;

frequencyMin=(frequencyMin==frequency)?frequencyMin:(frequencyMinfrequency)?frequencyMax:frequency;

frequencyMin=(frequencyMin==–totalBits)?frequencyMin:(frequencyMin–totalBits)?–totalBits:–totalBits;

frequencyMin=(frequencyMin==–totalBinCount[–bin])?(bin):(–bin);

frequencyMax=(frequencyMax==–bin)?(bin):(bin)–,(bin)==(bin)–,(bin)==(bin)–,(bin)==(bin)–,(bin)==(bin)–,(bin)==(bin)–,(bin)==(bin)–,(bin)==(bin)–?(++(++(++(++(++(++(++(++(+(-(-(-(-(–(–(–(–(–(–(–(–(–(–(-(-(–)–)–)–)–)–)–)–)–)–)–
–)))))))))))))):(((++(++(++(++(++(++(++(++(+(-(-(-(-(–(–(–(–(–(
–)))))))))))))))))));

frequencyMin=(frequencyMin==(++++(+((-((-((-((-((-(-(–
–))))))
–))))))
–))?(((++(+((-((-((-((-(-(–
–))))))
–))))))
–)):((((+++(((+++(
(+(+(
(+(+(
(+(+(
(+(+(
(+(+(
+++
)))))
)))))
)))))
)))))
)))));

frequencyMax=(((((+++
((+++(
(+++
(+++
(+++
(+++
(+++
(+++
(+++
(+++

((++++(((++++((
+++((
++(

((((((((((((((((((((((((((++++++++++
))))
))))
))))
))
))))
))))
))))
)++
)
)
++
)
++
)
++
)
++
)
++
)
++
)
++
)
++
)
+
))
))
))
))
))
))
))
))
)++)
)++)
)++
)++
)++
)++
)++
)++)
)))++))))))))))
)))++
)))++
)))++
)))++
)))++
)))++

);
);

frequencyMinMax=(((((+++
((+++(
(+++
(+++
(+++
(+++
(+++
(+++
(+++

((++++(((++++((
+++((
++(

((((((((((((((((((((((((((++++++++++
))))
))))
))))
))
))))
))))
))))
)++)
)
++)
++)
++)
++)
++)
++)
++)
++)
++)
++)
++)
++)
++)
++)
++)

;););););););););););););))))

);));));));));));));));));

));)));)));)));)));

));)));)));)));));

));)));)));)))

));
));
));
));
));
));
));

);
);
);
);

);
);
);

);
);

;
;
;

;
;
;

;
;
;

;;;;;
;;;;;
;;;;;
;;;;;
;;;;;

;;…
;;…
;;…
;;…

…
…
…
…

…..
…..
…..
…..

……….
……….
……….
……….

}
else{
samples[SAMPLE_HIST_LEN]=samples[SAMPLE_HIST_LEN-1];
}

SAMPLE_HIST_INDEX–;

sampleAverage=((double)(SAMPLE_HIST_LEN*(SAMPLE_HIST_LEN+1))/200000000)*sampleAverage+sample/(double)(SAMPLE_HIST_LEN*(SAMPLE_HIST_LEN+1));

sampleVariance=((double)(SAMPLE_HIST_LEN*(SAMPLE_HIST_LEN+1))/200000000)*sampleVariance+(pow(sample-sampleAverage,2)/(double)(SAMPLE_HIST_LEN*(SAMPLE_HIST_LEN+1)));
sampleStdDev=sqrt(sampleVariance);

sampleHist[sampleHistIndex++]=samples[0];

sampleHist[sampleHistIndex++]=samples[sampleHistIndex];

sampleHist[sampleHistIndex++]=samples[sampleHistIndex];

}
}

int main(){
int i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e],
w,HistogramArray[HISTOGRAM_ARRAY_LENGTH]={HistogramArray[HISTOGRAM_ARRAY_LENGTH]}={HistogramArray[HISTOGRAM_ARRAY_LENGTH]}={HistogramArray[HISTOGRAM_ARRAY_LENGTH]}={HistogramArray[HISTOGRAM_ARRAY_LENGTH]}={HistogramArray[HISTOGRAM_ARRAY_LENGTH]}={HistogramArray[HISTOGRAM_ARRAY_LENGTH]}={HistogramArray[HISTOGRAM_ARRAY_LENGTH]}={HistogramArray[HISTOGRAM_ARRAY_LENGTH]}={HistogramArray[HISTOGRAM_ARRAY_LENGTH]},
x,YAxisLength[YAXIS_LENGTH]={YAxisLength[YAXIS_LENGTH]}={YAxisLength[YAXIS_LENGTH]},
y,XAxisLength[XAXIS_LENGTH]={XAxisLength[XAXIS_LENGTH]},
z,XAxisScaleFactor=XAxisScaleFactor,
AverageFrequencyRange[AVERAGE_FREQUENCY_RANGE]={AverageFrequencyRange[AVERAGE_FREQUENCY_RANGE]},
Bandwidth=BANDWIDTH,
CumulativePower=CUMULATIVE_POWER,
DeltaFrequency=DELTA_FREQUENCY,
EnergyEnergy=Energies[Energies[Energies[Energies[Energies[Energies[Energies[Energies[Energies[Energies[Energies]]]]]]]]],
Frequencies[FREQUENCIES]={Frequencies[FREQUENCIES]},
GaussianGaussian=Gaussian[Gaussian[Gaussian[Gaussian[Gaussian[Gaussian[Gaussian[Gaussian[Gaussian[Gaussian]]]]]]]],
HistogramWindowSize=HistoGramWindowSize,

IFFTBufferSize=iFFTBufferSize,

JitterCorrectionThreshold=jitterCorrectionThreshold,

KernalSize=kernalSize,

Luminance=Luminance,

MaximalEnergyValue=maximalEnergyValue,

NoiseFloorNoiseFloor=noiseFloorNoiseFloor,

OverallSignalPowerOverallSignalPower=overallSignalPowerOverallSignalPower,

PeakDetectionThresholdPeakDetectionThreshold=peakDetectionThresholdPeakDetectionThreshold,

QuantizedQuantizedQuantizedValuesQuantizedQuantizedValuesQuantizedValues=[QuantizedQuantizedValuesQuantizedValuesQuantizedValues],

RelativeEnergyRelativeEnergyRelativeEnergy=[RelativeEnergyRelativeEnergyRelativeEnergy],

SampleRate=SAMPLE_RATE,

AverageFrequencyRange=AVERAGE_FREQUENCY_RANGE,

Bandwidth=BANDWIDTH,

CumulativePower=CUMULATIVE_POWER,

DeltaFrequency=DELTA_FREQUENCY,

EnergyEnergy=Energies[Energies[Energies[Energes]],Energiee,Energiee,Energiee,Energiee,Energiee,Energiee,Energiee,Energiee],Energiee],

Frequencies=Frequencies[FREQUENCIES],Frequencie,Frequencie,Frequencie,Frequencie,Frequencie,Frequencie,Frequencie,Frequencie],

GaussianGaussian=Gausian,Gausian,Gausian,Gausian,Gausian,Gausian,Gausian,Gausian,Gausian],

HistogramWindowSize=histoGramWindowSize,

IFFTBufferSize=iFFTBufferSize,

JitterCorrectionThreshold=jitterCorrectionThreshold,

KernalSize=kernalSize,

Luminance=luminance,

MaximalEnergyValue=maximalEnergyValue,

NoiseFloorNoiseFloor=noiseFloorNoiseFloor,

OverallSignalPowerOverallSignalPower=overallSignalPowerOverallSignalPower,

PeakDetectionThresholdPeakDetectionThreshold=pEakDetectioNThresHold,

QuantizedQuanTizEdQuanTizEdQuanTizEdValuesQuanTizEdQuanTizEdQuanTizEdVaLues=[QuaNTizEdVaLuesQuaNTizEdVaLuesQuaNTizEdVaLues],[quaNTizEdVaLuesquaNTizEdVaLuesquaNTizEdVaLues],[quaNtizeDvaLuEsquaNtizeDvaLuEsquaNtizeDvaLuEs],[quanTizeDvaLuEsquanTizeDvaLuEsquanTizeDvaLuEs],[quAnTiZeDvAlUesqUAnTiZeDvAlUesqUAnTiZeDvAlUes],[qaUnTiZeDaLuEsaQaUnTiZeDaLuEsaQaUnTiZeDaLuEs],[qAunTiZedALuESqAunTiZedALuESqAunTiZedALuES],[QAuntIzedAlueSAQAuntIzedAlueSAQAuntIzedAlueS],[QauNTIZEDALUESQauNTIZEDALUESQauNTIZEDALUES]],

RelativeEnergyRelativeEnegyRelativEEnegyRelativEEnegy=[relativEnegyrelativEnegyrelativEnegy,[reLaTIveNEgyreLaTIveNEgyreLaTIveNEgy],[ReLaTIveNEgyReLaTIveNEgyReLaTIveNEgy],[reLatIveNeGyrElAtIveNeGyrElAtIveNeGy]],[[ReLatIveNeGyrElAtIveNeGyrElAtIveNeGy]],[[ReLatIveNeGyrElAtIveNeGyrElAtIveNeGy]],[[ReLatIveNeGyrElAtIveNeGyrElAtiveNeGY]],[[ReLaTIvENegYrELATivenEGYrELATivenEGY]],

SampleRate=SAMPLE_RATE,

YAxisLength=Yaxislength,YaxIslength,YaxIslength,YaxIslength,YaxIslength,YaxIslength,YaxIslength,YaxIslength,YaxIslength],YaxIslengtH],YaxIslengtH],YaxIslengtH],YaxIslengtH],

XAxisLength=xaxislenght,XAxIslength,XAxIslength,XAxIslength,XAxIslength,XAxIslength,XAxIslength,XAxIslength],

XAxisScaleFactor=xaxisscalefactor,

AveragesOfAllFrames=[averagesOfAllFramesaveragesOfAllFramesaveragesOfAllFramesaveragesOfAllFramesaveragesOfAllFramesaveragesOfAllFramesaveragesOfAllFramesaveragesOfAllFrames],

BinsForSpecgram=[binsForSpecgrambinsForSpecgrambinsForSpecgrambinsForSpecgrambinsForSpecgrambinsForSpecgrambinsForSpecgrambinsForSpecgram],

CumulativeSumOverTime[cumulativeSumOverTimecumulativeSumOverTimecumulativeSumOverTimecumulativeSumOverTimecumulativeSumOverTimecumulativeSumOverTimecumulativeSumOverTimecumulativeSumOverTime],

FrameNumber[frameNumberframeNumberframeNumberframeNumberframeNumberframeNumberframeNumberframeNumberframeNumber],

HistogramArrays[historamArrayshistoramArrayshistoramArrayshistoramArrayshistoramArrayshistoramArrayshistoramArrayshistoramArrays],

IndexIntoCumulativeSums[indexIntoCumulaTeVSumsindexIntoCumulaTeVSumsindexIntoCumulaTeVSumsindexIntoCumulaTeVSumsindexIntoCumulaTeVSumsindexIntoCumulaTeVSumsindexIntoCumulaTeVSumsindexIntoCumulaTeVSums],

PowerLevels[powerLevelspowerLevelspowerLevelspowerLevelspowerLevelspowerLevelspowerLevelspowerLevelspowerLevels],

SpectrumHistory=spectrumhistorySpectrumHistorySpectrumHistorySpectrumHistorySpectrumHistorySpectrumHistorySpectrumHistorySpectrumHistory,[specTraHistoryspecTraHistoryspecTraHistoryspecTraHistoryspecTraHistoryspecTraHistoryspecTraHistoryspecTraHistory]

};

return EXIT_SUCCESS;#ifndef __AUDIOSPECS__
#define __AUDIOSPECS__

#include “AudioSettings.h”
#include “AudioStream.h”
#include “AudioAnalyzer.h”

class AudioSpecs : public AudioAnalyzer{
public:
virtual void init();
virtual void update();
virtual void set(int channel,float level);
virtual int getMax();

private:
static const int SPECTRUM_SAMPLES_PER_FRAME=SPECS_SPSF,SPECS_SPSF,SPECS_SPSF,SPECS_SPSF,SPECS_SPSF,SPECS_SPSF,SPECS_SPSF,SPECS_SPSF,SPECS_SPSF,SPECS_SPSF,SPECS_SPSF,SPECS_SPSF,SPECS_SPSF,SPECS_SPSF,SPECS_SPSF,SPECS_SPSF;//number of spectrum samples per frame

static const float SPEC_MAX_LEVEL=SPEC_MAX_LEVEL;//maximum possible level

static const int SPEC_HISTORY_SIZE=SPEC_HISTORY_SIZE;//number of samples history buffer size

static const int SPEC_HISTORY_DEPTH=SPEC_HISTORY_DEPTH;//depth used when calculating average levels

static const float SPEC_DECAY_RATE=SPEC_DECAY_RATE;//rate at which current levels decay back down

static const float SPEC_THRESHOLD_LEVEL=SPEC_THRESHOLD_LEVEL;//threshold below which channels are considered inactive

static const int SPECOAMPRANGE_START_FREQ_BIN=SPOECOMPRANGE_START_FREQ_BIN;//start bin number for amplitude range

static const int SPECOAMPRANGE_END_FREQ_BIN=SPOECOMPRANGE_END_FREQ_BIN;//end bin number for amplitude range

static const float SPECOAMPRANGE_MIN_RELATIVE_AMPLITUDE=SPOECOMPRANGE_MIN_RELATIVE_AMPLITUDE;//minimum relative amplitude within range

static const float SPECOAMPRANGE_MAX_RELATIVE_AMPLITUDE=SPOECOMPRANGE_MAX_RELATIVE_AMPLITUDE;//maximum relative amplitude within range

static const float SPECOAMPHISTORY_DECAY_RATE=SPOECOMPHISTORY_DECAY_RATE;//rate at which history values decay

private:
struct Spec_Channel{
public:
float current_level,current_level,current_level,current_level,current_level,current_level,current_level,current_level,current_level,current_level,current_level,current_level,current_level,//stores current energy level after decaying previous value

float average_energy_average_energy_average_energy_average_energy_average_energy_average_energy_average_energy_average_energy_average_energy,//average energy over history depth frames

float maximum_amplitude_maximum_amplitude_maximum_amplitude_maximum_amplitude_maximum_amplitude_maximum_amplitude_maximum_amplitude_maximum_amplitude,//maximum amplitude ever reached since last reset

bool active_active_active_active_active_active_active_active_active_active_active_active,//indicates whether channel is active

int frame_index_frame_index