Dynamic Ringtone Adjustment using On Demand Sampling on Android Smartphones

Casey O’Leary – Washington State University

CURENT REU

Mentor: Yong Li

Project Manager: Dr. Wei Gao

7/17/2014

Knoxville, Tennessee

Overview

2

Goal

Algorithm

Source Code Integration

Testing

Future Work

Goals of Research

• Implement current method of sampling and adjusting in the Android Kernel

• Adjust the current method to implement demand sampling (done)

• Improve upon current method using frequency analysis (future work)

• Originally created and developed by Christopher Daffron and Alex Hoppe

3

Algorithm for Sampling and Processing

Frequency and Intensity Categorized

Contrasting ringtone selected Volume set based on Intensity level

Calculating Frequency and IntensityWeighted Average Frequency Average Intensity

Fast Fourier Transform Simplicity vs. Speed Princeton CS Library

On Demand Sampling500 millisecond audio sample Raw audio data stored in WAV file

4

Frequency and Intensity Calculations

• Weighted average frequency – calculated by takes the summation of intensity at each frequency multiplied by the frequency and then divide that by the summation of all frequency intensity values

• for(int i = 0; i < transformData.length/2; i++) {

numVals++;

rawSum = rawSum+ transformData[i].abs();

sum = sum + transformData[i].abs()*I;

}avgVal = sum / rawSum;• Average Intensity – calculated by taking the average value of the absolute

value of all of the samples• for(int i = 0; i < channel1Data.length; i++) {

sum = sum + Math.abs(channel1Data[i]); counter++; } avIntensity = sum / counter;

5

Ringtone and Intensity Thresholds

• Frequency Thresholds

• Intensity Thresholds

6

2000 Hz

• Cat 1 <• -> 3

3300 Hz

• Cat 2 <• -> 4

4500 Hz

• Cat 3 <• -> 5

6000 Hz

• Cat 4 < • -> 4

6000 Hz <

X• Cat 5• -> 1

< .01

2• Stream

0

< .03

0• Stream

1

< .04

8• Stream

2

< .06

7• Stream

3

< .08

5• Stream

4

< .10

3• Stream

5

< .14

• Stream 6

.14 < X

• Stream 7 (Max)

Reasoning for Thresholds

• Device specific – Using LG Nexus 4

• Microphone samples audio at 44,100Hz

• .5s provides 22,000 samples

• Different phone may need adjustment

7

Source Code Integration

• Overview of Android Architecture

8

Source Code Integration

Incoming Call -> Broadcast Message• Telephony Service broadcast receive• Abstract layer between phone application and radio hardware

Message is then handled in the CallNotifier class• Contains instance of Ringer class• Controls when the Ringer is played

Ringer.ring() Function• Called by CallNotifier class • Modifications placed within this function• Executed only before first ring using ring counter

9

Source Code Integration

Audio Sampling• Audio buffer Created• Start recording thread• 500 ms delay using SystemClock static methods• Recording thread stopped

Processing• Raw audio data put into WAV file• Fast Fourier Transform applied producing complex data• Frequency and Intensity Algorithms applied • Average Frequency and Intensity set and categorized• Series of Logic statements and switch statements set ringtone and

volume• Call UI displayed and ringing starts

10

Testing

11

1 2 3 4 50

1500

3000

4500

6000

Calculated Avg. Frequency in Varying Environments using Modified Android

System Driving at NightVideo Games and TVPadeo at NightWalking Hallway in HandWalking Hallway in PocketMarket Square Af-ternoon

Trial Run

Avera

ge F

requency

(Hz)

Testing

12

1 2 3 4 50

0.05

0.1

0.15

0.2

0.25

Calculated Average Intensity of Samples in Varying Environments

Driving at NightVideo Games and TVPadeo at NightWalking Hallway in HandWalking Hallway in PocketMarket Square Af-ternoon

Trial

Avera

ge I

nte

sity

Testing

• Algorithm Run Time

• About 150-200ms

longer than on

Ringtone Selector

Application

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Total Runtime of Algorithm for each sample (ms) Sample

35301B36252B36503B36304B35665B36481C35352C35093C35374C35265C36441D35532D36513D37004D37065D35471E35872E35823E36444E36175E38091F36292F37263F36574F37625F38091G36992G42803G45034G44145G

3709.166667Average Runtime (ms)

Conclusion and Future Work

Algorithm Optimization• Average runtime of about 3.5 seconds• Eliminate unnecessary complexity or looping• Use multi-threading strategy to process data

Noise Reduction using Signal Processing Techniques• Identify any unwanted “noisy” frequencies to get a more

accurate representation of environment

Data Processing via Cloud Computing• Use a cloud, such as Amazon EC2, to perform necessary data

processing• Need to consider wireless signal strength and speed to

determine if this method would be efficient

14

Questions?

15